Gong

A method based on multi-source feature detection for counting people in crowded areas

Keywords: HOG, LBP, SVM, CANNY I

We propose a crowd counting method for multisource feature fusion. Image features are extracted from multiple sources, and the population is estimated by image feature extraction and texture feature analysis, along with for crowd image edge detection. We count people in high-density still images. For instance, in the city's squares, sports fields, subway stations, etc. Our approach uses a still image taken by a camera on a drone to appraise the count in the population density image, using a kind of sources of information: HOG, LBP, CANNY. We furnish separate estimates of counts and other statistical measurements through several types of sources. Support vector machine SVM, classification and regression analysis, along with obtain a high density population, reasonable early warning, to ensure the safety of the population. This method can achieve bon results in scenes where people are extremely crowded.

INTRODUCTION

Counting the number of people in these years has become a very hot topic because people are paying more and more attention to safety awareness. With the improvement of living standards, a kind of entertainment activities are increasing, and it also brings hidden dangers to people's safety. Then, it is extremely important to accurately appraise the number of people in the public area for security control, to prevent people in some areas from exceeding a certain level, and to give early warning. On December 31, 2014, it was the New Year's Eve event. As many tourists gathered in the Shanghai Bund to greet the New Year, some people lost their balance and fell on the viewing platform, which led to many people falling and stacking, causing a crowded stampede. For preventing such accidents, when some areas exceed a certain number of people, the number of people can be accurately estimated in real time, and the safety measures can be quickly taken to evacuate the crowd through the form of drone warning or large-scale broadcast broadcasting.

In this work, we offer a crowd counting approach for multi-source feature fusion. Image features come from multiple sources of information [START_REF] Li | Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[END_REF], [START_REF] Fradi | Low level crowd analysis using frame-wise normalized feature for people counting[END_REF], and the population is estimated by image feature extraction and texture feature analysis, along with for crowd area image edge detection. We count crowd people in high-density still images. The remainder of this article is expressed as follows. In Section 2, we briefly review the related work. In Section 3, we outline the proposed approach. In Section 4, we evaluated our benchmarks, experimental data, and databases. In Section 5, we summarize the paper.

II. RELATED WORK

There are many methods that have been used for crowd detection and counting. Some of the efficient techniques are:

The HOG feature is used to detect the human head. Dalal et al. first use the HOG feature for the pedestrian detection in the static image. The main thought used the gradient histogram of the local region to describe the target feature. HOG features combined with SVM classifier for head detection, SVM classifier training and head detection [START_REF] Li | Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[END_REF][START_REF] Zhang | Attention to Head Locations for Crowd Counting[END_REF].

A new method for population counting and density estimation. The CNN-based approach is further categorized according to the training process and network attributes. Select a representative subset of the latest methods for detailed analysis and review. In addition, we observed that combining the ratio and context information in the CNNbased approach greatly improved the estimation error [START_REF] Sindagi | A survey of recent advances in cnnbased single image crowd counting and density estimation[END_REF].

Based on different framework structures, individuals are detected by grouping local blocks in the region [START_REF] Rodriguez | Density-aware person detection and tracking in crowds[END_REF][START_REF] Hou | Human detection in crowded scenes[END_REF][START_REF] Fradi | Low level crowd analysis using frame-wise normalized feature for people counting[END_REF].

To used multiple sources of information to compute a measure of the number of individuals present in a severely dense crowd visible in a single image. Such as texture elements (using SIFT), and frequency-domain analysis to estimate counts [START_REF] Le | Human-Crowd Density Estimation Based on Gabor Filter and Cell Division[END_REF][START_REF] Kim | Crowd Density Estimation Using Multi-class Adaboost[END_REF][START_REF] Marana | Estimation of crowd density using image processing[END_REF][START_REF] Idrees | Multi-source Multiscale Counting in Extremely Dense Crowd Images[END_REF].

An adaptive smoothing algorithm in accordance with Canny operator is combined with global and local edge detection to extract the edge features of the target. The local region detection method is selected for edge extraction. The complete image edge is achieved by the edge detection method, which combines global and local edge features [START_REF] Yuan | Adaptive Image Edge Detection Algorithm Based on Canny Operator[END_REF].

III. PROPOSED SYSTEM

We propose a population counting method for feature fusion and edge detection. By image feature extraction and texture feature analysis, and for crowd head edge detection, the fusion of counts from multiple sources is used to estimate the count. We use high-density still images to calculate the number of people in the crowd.

A. HOG based Head Detections

HOG initially proposed by Dalal et at for Pedestrian Detection is essentially gray gradient statistics of local image regions [START_REF] Li | Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[END_REF], [START_REF] Zhang | Attention to Head Locations for Crowd Counting[END_REF]. As HOG feature sand Haar-like features utilize gradient information on targets, they are robust to noise and illumination changes. But because basic description, features including HOG and Haar-like ones are relatively simple, in many cases, they are weak in describing complex image regions. To improve on this, we propose a population counting method for feature fusion and edge detection. By image feature extraction and texture feature analysis, and for crowd head edge detection, the fusion of counts from multiple sources is used to estimate the count.

Assume the pixel located at (x, y) has a gray value I, a gradient magnitude G, and a gradient direction θ. For simplicity, we adopt a one-dimensional center gradient operator indicated as [-1, 0, 1]. More precisely, the horizontal gradient and vertical gradient are defined as follows:

Gx(x,y) = I(x + 1,y)-I(x-1,y)

(1)

Gy(x,y) = I(x,y + 1)-I(x,y-1) (2)

And the gradient of the pixel located at (x, y) calculated as follows:

𝐺(𝑥, 𝑦) = √𝐺 𝑋 (𝑥, 𝑦) 2 + 𝐺 𝑦 (𝑥, 𝑦) 2 (3) 𝜃(𝑥, 𝑦) = tan -1 ( 𝐺 𝑋 (𝑥, 𝑦) 𝐺 𝑦 (𝑥, 𝑦) )

Afterwards, a HOG is constructed for each cell unit. We divide the image into several "cells" [START_REF] Zhang | Attention to Head Locations for Crowd Counting[END_REF][START_REF] Sindagi | A survey of recent advances in cnnbased single image crowd counting and density estimation[END_REF], for each cell consisting of 8x8 pixels the gradient magnitudes are weighted according to their gradient direction and then accumulated in their bin direction, resulting in a gradient histogram. We choose a directional range of 0-360 degree for HOG features of pedestrians, with 9 segments of 40 degree and correspondingly 9 bin directions.

Finally, the obtained HOG feature vectors are normalized so as to uniformly express features of different blocks.

We consult the normalized block descriptors (vectors) as HOG descriptors [START_REF] Idrees | Multi-source Multiscale Counting in Extremely Dense Crowd Images[END_REF]. Through this method, the HOG descriptor becomes a vector consisting of the histogram components of all cell units in each interval. A vector with HOG feature dimensions.

B. LBP feature

The LBP were first proposed by T. Ojala, M.Pietikäinen, and D. Harwood. LBP is a simple but very effective texture operator [START_REF] Hou | Human detection in crowded scenes[END_REF]. It compares each pixel with its nearby pixels and saves the result as a binary number. The most major advantage of LBP is its robustness to changes in grayscale such as illumination changes. Its other important feature is its simple calculation, which makes it possible to analyze the image in real time. The essential LBP operator is defined as the 3*3 window. Using the value of the center pixel of the window as the threshold, the gray value of the adjacent 8 pixels is compared with it. If the surrounding pixel value is greater than the center pixel value, the pixel value is of the location is marked as '1'. Otherwise, it is '0'. In this fashion, the 8 points in the 3*3 neighborhood can be compared to produce 8-bit binary numbers (usually converted to decimal numbers, is 256 types of LBP codes), which is to get the LBP value of the pixel in the center of the window, and use this value to reflect the texture information of the area, for example: 00010011. Each pixel has 8 adjacent pixels, and 2 8 possibilities.

The essential LBP feature for a given pixel is taking shape by thresholding the 3x3 neighbourhood with the center pixel value as the threshold, where(Xc,Yc) is the center pixel, ic be the intensity of the center pixel and in (n=0,1,2....7) pixel intensities from the neighborhood. The LBP is given by:

𝐿𝐵𝑃(𝑋 𝐶 , 𝑌 𝐶 ) = ∑ 2 𝑛 (𝑖 𝑛 -𝑖 𝑐 ) 𝑃-1 𝑛=0 ( 5
)
Where P is the number of sample points and:

𝑠(𝑥) = { 1 𝑖𝑓 𝑥 ≥ 0 0 𝑒𝑙𝑠𝑒 (6)
The LBP could be interpreted as an 8-bit integer. The essential LBP concept is presented in Figure 1 Figure 1.Illustration of the standard LBP operator. Image taken from the Daimler pedestrian dataset.

C. Canny based head edge detection

Analysis of the principles of the Canny edge detection algorithm of the Canny operator in the Gaussian filtering process were in-depth study [START_REF] Yuan | Adaptive Image Edge Detection Algorithm Based on Canny Operator[END_REF], and to use first order partial derivatives of the finite difference to calculate the magnitude and direction of the gradient is extended, are calculated by using the four directions, effectively improve the precision of Canny edge detection.

First of all, denoise by using, Gaussian filtering, Its purpose is to smooth the original image and remove or weaken the noise in the image. Assuming two dimensional Gauss's function:

𝐺(𝑥, 𝑦) = 1 2𝜋𝜎 2 exp [- 𝑥 2 + 𝑦 2 2𝜎 ] (7)
Gradient vector:

∇𝐺 = [ 𝜕𝐺/𝜕𝑥 𝜕𝐺/𝜕𝑦 ] ( 8
)
To improve the speed of the decomposition method, the 2 filters at convolution template G is decomposed into 2 onedimensional filters:

𝜕𝐺(𝑥,𝑦,𝜎) 𝜕𝑥 = 𝑘𝑥𝑒 𝑥 2 2𝜎 2 𝑒 𝑦 2 2𝜎 2 = ℎ 1 (𝑥)ℎ 2 (𝑦) (9)
𝜕𝐺(𝑥,𝑦,𝜎)

𝜕𝑦 = 𝑘𝑥𝑒 𝑥 2 2𝜎 2 𝑒 𝑦 2 2𝜎 2 = ℎ 1 (𝑦)ℎ 2 (𝑥) ( 10
)
Where k is a constant, 𝜎 is Gaussian filter parameters. It controls the degree of smoothing. The 𝜎 filter, although the positioning accuracy is high, but the signal to noise ratio is low; 𝜎 is the opposite, so according to the need to adapt to the selection of Gaussian filter parameter.

We use a new 3x3 neighborhood in calculating the gradient amplitude. The procedure is as follows:

The (17)

The gradient direction is:

𝜃(𝑥, 𝑦) = arctan ( 𝑓 𝑥 (𝑥, 𝑦) 𝑓 𝑦 (𝑥, 𝑦) ) ( 18
)
The method takes into account the pixel diagonal direction, increase the pixels to computer the partial derivative direction, improves the traditional canny operator, uses differential mean value calculation, and improve the accuracy of the positioning of the edge.

Then, non-maxima suppression of gradient amplitude: the gradient of the non-maxima suppression [START_REF] Marana | Estimation of crowd density using image processing[END_REF], only according to the gradient magnitude image is not enough to determine the edge, in order to determine the edge must be in the refinement of the gradient magnitude image ridge belt, so as to produce the refinement edge. Non-maxima suppression through inhibition of gradient direction on the non-roof peak of the gradient magnitude to refine the gradient magnitude of the roof.

Finally, dual threshold detection and edge linking: nonmaximum suppression, the gradient array processing after thresholding.

Marginal discriminant: every edge intensity above a high threshold of edge points; every edge strength less than low threshold is definitely not the edge points; if the edge strength is higher than the lower threshold than the high threshold, then look at the pixel adjacent pixels in no more than the high threshold of edge points and if there is, it is the edge point, if not, it is not the edge points.

Through the Canny operator's detection of the head image, we can get a clear head outline shown in figure 2: By optimizing the Canny operator, the effect of noise on the image can be reduced, and the image data can be better preserved, making it easier to find the outline of the head image for extraction.

D. Training of joint HOG-LBP-CANNY classifiers

Feature extraction is one of the most critical aspects of human head detection. Extracting features with distinguishing significance plays an important role in the accurate detection of the human head. Our work integrates the features of HOG and LBP, which not only combines the effective identification information of multiple features, but also eliminates most of the redundant information, thereby realizing effective compression of information, saving information storage space, and facilitating the acceleration of operations and real-time processing of information. Here we use a serial fusion approach, as provided in Figure 3: Here, we use SVM [START_REF] Li | Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[END_REF], [START_REF] Le | Human-Crowd Density Estimation Based on Gabor Filter and Cell Division[END_REF] to achieve optimal classification of linearly separable data. For a linear SVM with the training samples ((xi,yi)),1 ≤ i ≤ N ), where xi is the ith instance sample, yi is the corresponding category labels (i.e., the expected response),its decision surface equation can be expressed as:

𝜔 • 𝑥 + 𝑏 = 0 ( 19
)
Where x is the input vector, 𝜔 is the dynamically variable weight vector, and b is the offset. In essence to find an optimal classifier is to find an optimal hyper plane according to formula, which can not only separate two classes correctly but also maximize the between-class distance. Accordingly, support vectors refer to the training sample points located in the classification boundaries, which are the key elements of the training sample set. Based on these theories and concepts, the following formula is used to classify the input samples:

𝑓(𝑥) = 𝑠𝑔𝑛 {∑ 𝑎 𝑖 𝑦 𝑖 (𝑥 𝑖 • 𝑥) + 𝑏 𝑘 𝑖=1 } ( 20
)
where ai is the weight coefficient corresponding to the support vector xi.

The next step, we connect the sample HOG feature vector and the LBP feature vector in series to form a joint feature vector input SVM. Here, in the classification process, the linear inseparable low-dimensional space is converted into a linearly separable high-dimensional space mainly through SVM kernel functions and use the cross-validation method to select the SVM optimal parameters, so that the classifier has the highest classification accuracy of the input training samples.

It is shown by the study of the human visual system, image boundary is particularly important, often a rough outline of the line alone can by identifying an object [START_REF] Kim | Crowd Density Estimation Using Multi-class Adaboost[END_REF]. This fact for machine vision research provides important enlightenment, namely: objects available the boundary represented by the grayscale image discontinuous points consisting of basic original carrying the original image of the vast most useful information.

Here, we obtain a clear head contour, fusion feature extraction and texture feature analysis by edge detection of the header image, and combine the information sources from multiple sources to estimate the counting population. The process is as follows:

E. Experimental Results

In order to objectively verify the performance of the proposed algorithm, some experiments and some frequently used algorithms were performed. Our experiment is mainly divided into two parts: the first part is for pedestrian detection, the second part count the population of pictures of still people.

The first part of the experiment: The training set we used to contained 5000 head face images clipped manually and enough non-head face images from some sample sets including INRIA, PETS2000, and MIT. During training, negative samples can be selected as needed automatically from the background images. The test set contains 5000 images with or without pedestrians, including about 4,500 apparent head face and covering various scenes, angles, postures, and clothing, etc. The algorithm is embodied in a program developed with Matlab 2017a function library.

Extract sample HOG, LBP texture features and CANNY head contour edge detection:

Sample HOG feature calculation steps: For each positive and negative sample set, each size of 32*32 grayscale pictures (in this case, the grayscale picture is used to consider the effect of the measurement of the calculation, and the final detection result has little effect), and the rectangular HOG feature is calculated. Descriptor: The set cell size is 8*8, the measurement of the block is 16*16, and the slide step size is the width of a Cell. Count all the blocks in the tandem image and calculate the dimensions of the HOG feature vector.

Sample LBP feature calculation steps: For each positive and negative sample set, each size is a 32*32 grayscale image, and LBP feature extraction based on a sliding window is used. The general description of the sliding window for the image algorithm is as follows: In an image of size W*H, the w*h window (W>>w, H>>h) is moved according to a certain rule, and a pixel in the window is performed. In the series operation, the window moves one step to the right or down after the operation is completed, until the entire image is processed. Set the size of the window to 16*16, and set the window's horizontal and vertical sliding steps to half the width of the window. The statistical histogram of all windows in the series image, and the calculated dimension of the LBP feature vector.

Sample CANNY head contour edge detection: use high and low thresholds to find edge points, reduce the number of false edge segments, and get the sample head contour image.

Then, the sample HOG feature vector and the LBP feature vector are connected in series to form a joint feature vector. Add CANNY edge detection to extract the outline of the head. We use the SVM classifier to transform the linearly indivisible, low-dimensional space into a linearly separable, high-dimensional space by using a kernel function. The cross-validation method is used to select the optimal SVM parameters so that the classifier pairs the input training samples for a highest classification accuracy. In the second part of the experiment: we will process the image of the crowd and divide it into small pieces. For example, a set of 256 * 256 pixel pictures, each of which is defined by a size of 16 * 16 pixels, 32 * 32 pixels, 64 * 64 pixels, and 128 * 128 pixels, as provided in figure 5: We have tried many times and got the results given in Table II. In table II, the number of people detected, number of people actually present in the scene, the difference between the detected number of people and the actual number of people and time. Based on the above results, the precision calculated is 92.85\%.

Table II. Result summary.

F. Conclusion

In this paper, we propose a feature fusion method for population counting. A plurality of analytical detection methods, such as extraction of image features, edge detection of target feature images, and the like, so that data obtained by a plurality of information sources is used for calculating a population. Therefore, we use a variety of sources of information, namely HOG, LBP and CANNY. These sources provide separate combinations of estimates and statistical measurements. Using SVM classification techniques and regression analysis, we calculate highdensity populations. The approach adopted is easy and fast in processing. Compared to the joint HOG, our experiments showed the method gives good results in crowded scenes.

G. Future work

In order to improve the detection efficiency and apply it to the crowd testing in the real world, we plan to use the FPGA cards which is famous for its performance in real-time crowd image processing. Moreover, we installed it on the drone to count high-density people and provide early warning.

  partial derivatives of the 4 directions are calculated at first: Partial derivative of x direction: 𝑃 𝑥 (𝑥, 𝑦) = 𝐺(𝑥, 𝑦 + 1) -𝐺(𝑥, 𝑦 -1) (11) Partial derivative of y direction: 𝑃 𝑦 (𝑥, 𝑦) = 𝐺(𝑥 + 1, 𝑦) -𝐺(𝑥 -1, 𝑦) (12) 45 degree directional partial derivative: 𝑃 45 (𝑥, 𝑦) = 𝐺(𝑥 -1, 𝑦 + 1) -𝐺(𝑥 + 1, 𝑦 -1) (13) 135 degree directional partial derivative: 𝑃 135 (𝑥, 𝑦) = 𝐺(𝑥 + 1, 𝑦 + 1) -𝐺(𝑥 -1, 𝑦 -1) (14) Difference in horizontal direction: 𝑓 𝑥 (𝑥, 𝑦) = 𝑃 𝑥 (𝑥, 𝑦) + [𝑃 45 (𝑥, 𝑦) + 𝑃 135 (𝑥, 𝑦)]/2 (15) Difference in vertical direction: 𝑓 𝑦 (𝑥, 𝑦) = 𝑃 𝑦 (𝑥, 𝑦) + [𝑃 45 (𝑥, 𝑦) -𝑃 135 (𝑥, 𝑦)]/2 (16) The gradient magnitude is obtained: 𝑀(𝑥, 𝑦) = √𝑓 𝑥 (𝑥, 𝑦) 2 + 𝑓 𝑦 (𝑥, 𝑦) 2

Figure 2 .

 2 Figure 2.The original image (left) and the output (right) of the Canny algorithm.

Figure 3 .

 3 Figure 3.Joint HOG-LBP Histogram

Figure 4 .

 4 Figure 4.Crowd counting

Figure 5 .

 5 Figure 5.This figure shows the size of different pixel blocks used in this paper.Through the feature fusion of HOG-LBP, CANNY is used to extract the edge of the head contour and combined with the SVM classifier, the population density is estimated and counted for each small block image. The training set contains 5000 head facial images that were manually cut from the INRIA sample set. The experimental test image size is 600*400, and the experimental results are as figure6:

Figure 6 .

 6 Figure 6. This figure show one arbitrary image from the dataset used in this paper.

Table 1 :Table I .

 1I Algorithm performances shown by 4 experiments.

	The
	experimental test image size is 384×288. The algorithm is
	modified based on Matlab 2017a and runs on an Inter Core
	i5-5250 (1.60 GHz), 4 GB RAM computer. The
	experimental results are shown in


Gong

A method based on texture feature and edge detection for people counting in a crowded area

Keywords: HOG, LBP, SVM, CANNY. 1

We propose a population counting method for feature fusion and edge detection.The image is extracted from multiple information sources to estimate the count by image feature extraction and texture feature analysis, as well as for crowd head edge detection. We count people in high-density still images.For example, in the city bus station, subway. Our method uses a still image taken by the camera to estimate the count in the crowd density image, using multiple sources of information, namely: HOG, LBP, CANNY, these sources provides separate estimates of the number of counts and other statistical measures, through the support vector Machine SVM, classification, and regression analysis to obtain highdensity populations. This method can get good results in crowded scenes.

INTRODUCTION

Counting the number of people in these years has become a hot topic because people are paying more and more attention to safety awareness. With the improvement of living standards, there are countless kinds of entertainment activities, and it also brings hidden dangers to people's safety. In fact, it is extremely important to accurately estimate the number of people in public areas for security control, to prevent people in some areas from exceeding a certain level, and to provide early warning. A stampede on the Bund in Shanghai, China, takes place on New Year's eve, December 31, 2014. In order to prevent such accidents, some people in some areas need to accurately estimate the number of people in real time, take safety measures, evacuate the crowd, and avoid the occurrence of the stampede.

Related work

There are many methods that have been used for crowd detection and counting. Some of the efficient techniques are:



The HOG feature is used to detect the human head. a texture that corresponds to a harmonic pattern of fine scales. Counts from texture analysis methods: crowds are repetitive in nature since all human appear similar from a distance. We will adopt three different texture analysis methods which separately give an estimate of the count [START_REF] Le | Human-Crowd Density Estimation Based on Gabor Filter and Cell Division[END_REF][START_REF] Kim | Crowd Density Estimation Using Multi-class Adaboost[END_REF][START_REF] Marana | Estimation of crowd density using image processing[END_REF][START_REF] Idrees | Multi-source Multi-scale Counting in Extremely Dense Crowd Images[END_REF].



The Bayesian 3D model based method solves the problem of human detection in crowded scenes. Find the best configuration and its corresponding shape model by specifying the human head and foot detectors. In the model matching process, the solution is obtained in a way that balances the computational cost and the performance.

Results on various data show the effectiveness of the proposed method [START_REF] Wang | Bayesian 3D model based human detection in crowded scenes using efficient optimization[END_REF].



According to the people counting method based on head detection and tracking to evaluate the number of people who move under an over-head camera. The main methods are: foreground extraction, head detection, head tracking, and crossing-line judgment. Using an effective foreground extraction method to obtain mobile the foreground area of the person, and some morphological operations are used to optimize the foreground area. Based on the head tracking, a cross-line determination is used to determine if the head object is counted [START_REF]A People Counting Method Based on Head Detection and Tracking[END_REF][START_REF] Yang | A novel method for crowd density estimations[END_REF][START_REF] Ma | Crossing the Line: Crowd Counting by Integer Programming with Local Features[END_REF].



A novel integrated framework for the analysis of crowds including all relevant aspects as simulation, detection and tracking of pedestrians and dense crowds and event detection. It utilizes an appearance-based approach for object detection since this method has been successfully applied for very small objects. Track the pedestrian using the Bayesian tracking method. Graph-based event detection using hidden Markov models HMM [START_REF] Butenuth | Integrating pedestrian simulation, tracking and event detection for crowd analysis[END_REF].



The method of combining global with local edge detection to extract edge. Based on the adaptive smoothing algorithm of Canny operator, the local area detection method is selected for edge extraction. Local edge detection which uses a distance weighted average method based on kaverage method can overcome the impact of outliers on clustering effect. Complete image edge is getting through an edge detection method that combines global with local [START_REF] Yuan | Adaptive Image Edge Detection Algorithm Based on Canny Operator[END_REF].



Detect the crowd, use the point of interest detector to specify the image of the head area, and then count the number of people in the crowd [START_REF] Subburaman | Counting People in the Crowd Using a Generic Head Detector[END_REF][START_REF] Conte | A Method Based on the Indirect Approach for Counting People in Crowded Scenes[END_REF].



A deep-learning approach to estimate the number of individuals in the mid-level crowd visible in a still image.

The proposed deep-learning framework is used to learn a feature-count regression, which can estimate the number of people within each part, and the crowd estimation in the whole image is therefore the sum of that in all parts [START_REF] Hu | Dense crowd counting from still images with convolutional neural networks[END_REF][START_REF] Zhang | Cross-scene crowd counting via deep convolutional neural networks[END_REF].



Based on MID foreground segmentation module, it provides active areas of the head-shoulder detection module to detect head and count the number of people [START_REF] Li | Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[END_REF][START_REF] Rodriguez | Densityaware person detection and tracking in crowds[END_REF][START_REF] Hou | Human detection in crowded scenes[END_REF][START_REF] Fradi | Low level crowd analysis using frame-wise normalized feature for people counting[END_REF].

Proposed system

We propose a population counting method for feature fusion and edge detection. By image feature extraction and texture feature analysis, and for crowd head edge detection, the fusion of counts from multiple sources is used to estimate the count. We use highdensity still images to count the number of people.

HOG based Head Detections

HOG is used for head detection [START_REF] Li | Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[END_REF][START_REF] Le | Human-Crowd Density Estimation Based on Gabor Filter and Cell Division[END_REF]. Locally normalized HOG descriptors perform better than existing feature sets (including wavelets). Compared to the edge direction histograms and SIFT descriptors, the HOG is calculated on a grid-sized uniform cell units, and in order to improve performance, overlapping local contrast normalization is also used. In a static image, the representation and shape of the local target can be well described by the gradient or an edge direction density distribution. Its basis is the gradient of statistical information, and the gradient mainly exists on the edge of the space. In order to reduce the effect of light factors, we firstly normalize the image.

For the texture intensity of the image, the proportion of the local surface exposure is larger. Therefore, the compression process can effectively reduce the image of the local shadow and light changes.

Because the function of color information does not work well, images are usually converted to grayscale. We calculate the gradient of the image abscissa and the ordinate direction, as well as the gradient direction value for each pixel position. The guidance operation captures the edge, silhouette and some texture information, further weakening the lighting effects. The gradient of the pixel (x, y) in the image is:

      y H y x H G , 1 x , 1 y , x x     (1)       1 , 1 , , y     y x H y x H y x G (2)
Where Gx, Gy represents the horizontal gradient, vertical gradient, and H (x, y) pixel values of the pixel (x, y) in the input image. The gradient and gradient directions at the pixel (x, y) are:

      2 2 , , , y x G y x G y x G y x   (3)                  y x Gy y x G y x x , , tan , 1 (4)
A gradient orientation histogram is constructed for each cell unit. We divide the image into several "cells", each cell consisting of 8*8 pixels. Suppose we use nine bin histograms to count the 8*8 pixel gradient information. We divide the gradient of the cell by 360 degrees into nine directions. Due to changes in local illumination and changes in the foreground and background contrast, the range of gradient intensity is very large. This requires normalizing the gradient intensity. Normalization can further illuminate light, shadows, and edge compression. The approach we take is to combine the individual cell units into large blocks and spatially connected intervals. Normalizing the HOG feature vector in the block and introducing v to represent a vector that has not been normalized. It contains all histogram information for a given block. According to the ||V_{k}|| standard, where k is 1 or 2, and e is a small constant. At this point, the normalization factor can be expressed as follows: We refer to the normalized block descriptors (vectors) as HOG descriptors. In this way, the HOG descriptor becomes a vector consisting of the histogram components of all cell units in each interval. A vector with HOG feature dimensions.

LBP feature

The Local Binary Patterns (LBP) were first proposed by T. Ojala, M.Pietikäinen, and D. Harwood. LBP is a simple but very effective texture operator [START_REF]A People Counting Method Based on Head Detection and Tracking[END_REF]. It compares each pixel with its nearby pixels and saves the result as a binary number. The most important advantage of LBP is its robustness to changes in grayscale such as illumination changes. Its other important feature is its simple calculation, which makes it possible to analyze the image in real time. The basic LBP operator is defined as the 3*3 window. Using the value of the center pixel of the window as the threshold, the gray value of the adjacent 8 pixels is compared with it. If the surrounding pixel value is greater than the central pixel value, the pixel value is of the location is marked as '1'. Otherwise, it is '0'. In this way, the 8 points in the 3*3 neighborhood can be compared to produce 8-bit binary numbers (usually converted to decimal numbers, is 256 types of LBP codes), which is to get the LBP value of the pixel in the center of the window, and use this value to reflect the texture information of the area, for example: 00010011. Each pixel has 8 adjacent pixels, and 2 8 possibilities.

The basic LBP feature for a given pixel is formed by thresholding the 3x3 neighbourhood with the centre pixel value as the threshold, where(Xc,Yc) is the center pixel, ic is the intensity of the centre pixel and in (n=0,1,2....7) pixel intensities from the neighborhood. The LBP is given by:

         1 0 2 , P n c n n c c i i s Y X LBP (6)
Where P is the number of sample points and:

       else x if x 0 0 1 s (7)
The LBP could be interpreted as an 8-bit integer. The basic LBP concept is presented in Figure 2: When the LBP operator is used for texture classification or face recognition, the statistical histogram of the LBP mode is often used to express the image information, and more pattern types will make the data volume too large and the histogram too sparse. Therefore, it is necessary to reduce the dimension of the original LBP mode so as to best represent the image information in the case of a reduced data amount. In order to solve the problem of too many binary patterns and improve statistics, we proposed a "Uniform Pattern" to reduce the dimension of LBP operator's pattern. In an actual image, most LBP patterns only contain at most two transitions from '1' to '0' or from '0' to '1'. When a loop binary number corresponding to an LBP change from '0' to '1' or from '1' to '0' at most twice, the binary corresponding to the LBP is called an equivalent pattern class. For example, 00000000 (0 jumps), 00000111 (only one transition from '0' to '1'), and 10001111 (first jump from '1' to '0', then '0' to '1' and two jumps in total) are all equivalent modes class. The modes other than the equivalent mode class are classified as another class, called a mixed model class, such as 10010111 (a total of four transitions). With this improvement, the variety of binary patterns are greatly reduced without losing any information. The number of modes is reduced from the original 2P to P (P-1)+2, where P represents the number of sampling points in the neighborhood set. For the 8 sampling points in the 3*3 neighborhood, the binary pattern is reduced from the original 256 to 58. This allows the feature vector to have fewer dimensions and can reduce the effects of highfrequency noise.

Training of joint HOG-LBP classifiers

Feature extraction is one of the most critical aspects of human head detection. Extracting features with distinguishing the significance plays an important role in the accurate detection of the human head. Our work integrates the features of HOG and LBP, which not only combines the effective identification information of multiple features, but also eliminates most of the redundant information, thereby realizing effective compression of information, saving information storage space, and facilitating the acceleration of operations and real-time processing of information.

Here we use a serial fusion approach, as shown in Figure 3: Here, we use support vector machines to achieve the optimal classification of linearly separable data. For a linear SVM with the training samples {(xi,yi),1≤i≤N}, where xi is the ith instance sample, yi is the corresponding category labels(i.e.,the expected response),its decision surface equation can be expressed as:

0 x    b  ( 8
)
Where x is the input vector, ω is the dynamically variable weight vector,and b is the offset. In essence to find an optimal classifier is to find an optimal hyper plane, according to formula(8), which can not only separate two classes correctly but also maximize the between-class distance.

 

             k i i i i b x x y a x f 1 sgn (9)
where ai is the weight coefficient corresponding to the support vector xi.

The next step, we connect the sample HOG feature vector and the LBP feature vector in series to form a joint feature vector input SVM. Here, in the classification process, the linear inseparable low-dimensional space is converted into a linearly separable highdimensional space mainly through SVM kernel functions and use the cross-validation method to select the SVM optimal parameters, so that the classifier has the highest classification accuracy of the input training samples. In accordance with the above method, the training process for joint HOG-LBP SVM classifiers are shown in Figure 4:

Canny based head edge detection

Analysis of the principles of the Canny edge detection algorithm of Canny operator in the Gaussian filtering process were in-depth study [START_REF] Yuan | Adaptive Image Edge Detection Algorithm Based on Canny Operator[END_REF], and to use first order partial derivatives of the finite difference to calculate the magnitude and direction of the gradient is extended, are calculated by using the four directions, effectively improve the accuracy of Canny edge detection. First of all, denoise by using, Gaussian filtering, Its purpose is to smooth the original image and remove or weaken the noise in the image. Assuming two dimensional Gauss's function:

             2 exp 2 1 , 2 2 2 y x y x G (10)
Gradient vector:

To improve the speed of decomposition method, the 2 filter at convolution template G is decomposed into 2 one-dimensional filters:

      y h h e kxe x y x G a y a x 2 1 2 2 x , , 2 2 2 2      (12)       x h h e kxe y y x G a y a x 2 1 2 2 y , , 2 2 2 2      ( 13
)
Where k is a constant, σ is Gaussian filter parameters. It controls the degree of smoothing. The σ filter, although the positioning accuracy is high, but the signal to noise ratio is low; σ is the opposite, so according to the need to adapt to the selection of Gaussian filter parameter σ . We adopt a new 3x3 neighborhood in calculating the gradient amplitude. The procedure is as follows:

The partial derivatives of the 4 directions are calculated at first: Partial derivative of x direction:

      1 , 1 , , x     y x G y x G y x P (14)
Partial derivative of y direction:

      y x G y x G y x P y , 1 , 1 ,     (15)
45 degree directional partial derivative:

      1 - , 1 1 , 1 - , 45 y x G y x G y x P     (16)
135 degree directional partial derivative:

      1 - , 1 - 1 , 1 , 135 y x G y x G y x P     (17)
Difference in horizontal direction:

          2 / , ,
   (19)
The gradient magnitude is obtained:

      2 2 , , , y x f y x f y x M y x   (20)
The gradient direction is:

               y x f y x f y x y x , , arctan ,  (21)
The method takes into account the pixel diagonal direction, increase the pixels to compute the partial derivative direction, improves the traditional canny operator, uses differential mean value calculations, and improve the accuracy of the positioning of the edge. Then, non-maxima suppression of gradient amplitude:the gradient of the non-maxima suppression, only according to the gradient magnitude image is not enough to determine the edge, in order to determine the edge must be in the refinement of the gradient Non-maxima suppression through inhibition of gradient direction of the non-roof peak of the gradient magnitude to refine the gradient magnitude of the roof. Finally, dual threshold detection and edge linking:non-maximum suppression,the gradient array processing after thresholding. Marginal discriminant: every edge intensity above a high threshold of edge points; every edge strength less than low threshold is definitely not the edge points; if the edge strength are higher than the lower threshold than the high threshold, then look at the pixel adjacent pixels in no more than the high threshold of edge points and if there is, it is the edge point, if not, it is not the edge points. Through the Canny operator's detection of the head image, we can get a clear head outline shown in figure 5: We propose the optimization of the Canny operator. We use advanced the Gauss filter, its purpose is to the original image smoothing processing to remove or reduce the image noise. Then, the first order partial derivative of finite difference to calculate the gradient magnitude and direction of the expansion, the calculation of four directions, effectively improve the canny edge detection accuracy. Then, non-maxima suppression through inhibition of gradient direction on all non-roof peak gradient magnitude to refine the gradient magnitude. Finally, non-maxima suppression processing after the gradient amplitude array threshold value of the high and low threshold edge points, reduce the false edge segments Number, get the image. By optimizing the Canny operator, the effect of noise on the image can be reduced, and the image data can be better preserved, making it easier to find the outline of the head image for extraction.

            y / x / G G G

Training of joint HOG-LBP-CANNY classifiers

It is shown by the study of the human visual system, image boundary is particularly important, often a rough outline of the line alone can by identifying an object. This fact for machine vision research provides important enlightenment, namely: objects available the boundary represented by the grayscale image discontinuous points consisting of basic original carrying the original image of the vast most useful information. In addition, the edge of the seat easily integrated into a wide variety of object recognition algorithm, in a broad sense, all visual information processing process it can be summed up as a combination of discontinuities (edges) and matching two operations. Here, we obtain a clear head contour, fusion feature extraction and texture feature analysis by edge detection of the head image, and combine the information sources from multiple sources to estimate the counting population. The process is as follows:

Experimental Results

In order to objectively verify the performance of the algorithm proposed, some experiments were made for it and some frequently-used algorithms. Our experiment is mainly divided into two parts: the first part is the pedestrian detection, and the second part is the counting of the crowd pictures. In an image of size W*H, the w*h window (W>>w, H>>h) is moved according to a certain rule, and a pixel in the window is performed. In the series operation, the window moves one step to the right or down after the operation is completed, until the entire image is processed. Set the size of the window to 16*16, and set the window's horizontal and vertical sliding steps to half the width of the window. The specific process of the LBP feature calculation is as follows: For one pixel in each window, an LBP feature value is calculated using an operator LBP (representing a radius 1, a ring containing 8 neighborhoods, a uniform mode). According to the LBP eigenvalue calculated in the window, the histogram of each window is calculated, that is, the number of occurrences of each LBP eigenvalue, and then L2norm is used for normalization. The statistical histograms of all windows in the tandem image, the dimensions of the resulting LBP eigenvectors.

Sample CANNY head contour edge detection: Advanced the Gauss filter, its purpose is to the original image smoothing processing to remove or reduce the image noise. Then, the first order partial derivative of finite difference to calculate the gradient magnitude and direction of the expansion, the calculation of four directions, effectively improve the canny edge detection accuracy. Then, non-maxima suppression through inhibition of gradient direction on all non-roof peak gradient magnitude to refine the gradient magnitude. Finally, non-maxima suppression processing after the gradient amplitude array threshold value of the high and low threshold edge points, reduce the false edge segments Number, get sample head contour image.

Finally, the sample HOG feature vector and the LBP feature vector are connected in series to form a joint feature vector. Add CANNY edge detection to extract the outline of the head. We use the SVM classifier to transform the linear indivisible, lowdimensional space into a linearly separable, high-dimensional space by using a kernel function. The cross-validation method is used to select the optimal SVM parameters so that the classifier pairs the input training samples. The second part of the experiment: we will process the image of the crowd and divide it into small pieces. We have tried many times and got the results. In the table, number of people detected, number of people actually present in the scene, the difference between the detected number of people and actual number of people and time. Based on the above results, precision calculated is: 92.30%.

Conclusion

In this paper, we propose a feature fusion method for crowd counting. By image feature extraction and texture feature analysis methods, data obtained from multiple sources are used to count the crowd. Therefore, we use multiple sources of information, namely, HOG, LBP and CANNY. These sources provide separate estimates and other combinations of statistical measurements.

Using the support vector machine (SVM) classification technique, and regression analysis, we count the crowd with high density. The approach adopted is easy and fast in processing. Our experiments showed the method gives good results in crowded scenes.

Future work

In order to improve the detection efficiency and apply it to the crowd testing in the real world, we plan to use the FPGA cards which is famous for its performance in real-time crowd image processing.

7.

Figure 1 .

 1 Figure 1. This figure shows four arbitrary images from the dataset used in this paper.

  Dalal et al. (2005) first use the HOG feature for the pedestrian detection in the static image. The main idea is to use the gradient histogram of the local region to describe the target feature. HOG features combined with Support Vector Machine (SVM) classifier for head detection, SVM classifier training and head detection are divided into two stages [1].  Propose a model that combines sources of information extracted from the images. Dense crowds can be thought as DOI: http://dx.doi.org/10.17501........................................

  DOI: http://dx.doi.org/10.17501........................................

Figure 2 .

 2 Figure 2. Illustration of the standard LBP operator. The image is taken from the Daimler pedestrian dataset.

Figure 3 .

 3 Figure 3. Joint HOG-LBP Histogram.

  Accordingly,support vectors refer to the training sample points located in the classification boundaries, which are the key elements of the training sample set. Based on these theories and concepts, the following formula is used to classify the input samples: DOI: http://dx.doi.org/10.17501........................................

Figure 4 .

 4 Figure 4. Training process for joint HOG-LBP SVM classifiers.

  ://dx.doi.org/10.17501........................................ magnitude image ridge belt, so as to produce the refinement edge.

Figure 5 .

 5 Figure 5. The original image (left) and the output (right) of the Canny algorithm.

Figure 6 .

 6 Figure 6. Crowd counting .

  For example, a set of 256*256 pixel pictures, each of which is defined by a size of 1*1 pixel, 2*2 pixels, 4*4 pixels, and 8*8 pixels. Through the feature fusion of HOG-LBP, CANNY is used to extract the edge of the head contour, and combined with the SVM classifier, the population density is estimated and counted for each small block image. The training set contains 500 head facial images that were manually cut from the INRIA sample set. The experimental test image size is 595*350, and the experimental results are as figure 7:

Figure 7 .

 7 Figure 7. This figure shows four arbitrary images from the dataset used in this paper

  The first part of the experiment: The training set we used contained 500 head, face images clipped manually and enough non-head face images from some sample sets including INRIA, PETS2000, and MIT. During training negative samples can be selected as needed automatically from the background images. The test set contains 500 images with or without pedestrians, including about 1,500 apparent head face and covering various scenes, angles, postures, and clothing, etc. The algorithm is embodied in a program developed with Matlab 2017a function library.

	Extract sample HOG, LBP texture features and CANNY head
	contour edge detection:
	Sample HOG feature calculation steps: For each positive and
	negative sample set, each size of 32*32 grayscale pictures (in this
	case, the grayscale picture is used to consider the effect of the size
	of the calculation, and the final detection result has little effect),
	and the rectangular HOG feature is calculated. Descriptor: The set
	cell size is 8*8, the size of the block is 16*16, and the slide step
	size is the width of a Cell. The specific process of HOG feature
	calculation is as follows: To reduce the influence of lighting, the
	sample is first Gamma standardization of images. Then calculate
	the gradient of x and y directions for each pixel in the grayscale
	image, and use the [-1,0,1] template to calculate the direction and
	amplitude of the gradient. In each Cell, set the projection direction
	to 9 bins, and use the gradient magnitude of each pixel as the
	weight, and vote to count the weighted histograms of gradient
	directions of each Cell. The dimension of this histogram is 9. Four
	cells in a Block (with overlaps between Blocks) are normalized
	using L2-norm, and the gradient histograms of four cells are
	counted, and the dimension is 36. Finally, all blocks in the image
	are concatenated, and the dimension of the obtained HOG feature
	vector is calculated.
	Sample LBP feature calculation steps: For each positive and
	negative sample set, each size is a 32*32 grayscale image, and

DOI: http://dx.doi.org/10.17501........................................ LBP feature extraction based on a sliding window is used. The general description of the sliding window for the image algorithm is as follows:

Table 1 . Algorithm performances shown by 4 experiments.

 1

The highest classification accuracy. The experimental test image size is 384*288. The algorithm is modified based on Matlab 2017a and runs on an Inter Core i5-5250 (1.60 GHz), 4 GB RAM computer. The experimental results are shown in Table

1:

Table 2 . Result summary.

 2


Isoird Karine
email: karine@cegely.insa-lyon.fr

K Isoird

M Lazar

L Ottaviani

M L Locatelli

C Raynaud

D Planson

J P Chante

STUDY OF 6H-SiC HIGH VOLTAGE BIPOLAR DIODES UNDER REVERSE BIASES

Keywords: 6H-SiC, Bipolar diodes, Breakdown voltage, OBIC, JTE. _______________________________________________________________________________

Silicon carbide presents electrical properties suitable for many applications especially for high voltage devices. 6H-SiC P + NN + structures have been fabricated following ISE software simulation in order to block voltages as high as 1.5 kV. In particular, these diodes are realized by surrounding 2 the emitter by a p-type region called JTE (Junction Termination Extension). Electrical characterizations under reverse bias at room temperature and in various environments (air, silicone oil) show a premature breakdown of the diodes. This breakdown is localized at the emitter periphery. OBIC (Optical Beam Induced Current) measurements show a peak of photocurrent at the junction end, indicating the presence of a high electric field. These results show an effectiveness of 60 % of the JTE. This is probably related to a bad electrical activation of the implanted aluminum during the post-implantation annealing.

Introduction

Silicon carbide presents interesting electrical properties for many applications and especially for the high voltage devices. To benefit the advantages of SiC and avoid any premature breakdown of the high voltage diodes, it is necessary to realize efficient junction termination. The object of this work is to estimate the JTE influence on the electrical behavior of the 6H-SiC P + NN + diodes under reverse biases. In this paper we present current-voltage characteristics (I-V) measured in different ambient (air, silicone oil). These results are completed by OBIC (Optical Beam Induced Current) measurements. This technique allows to investigate the electric field distribution in the structure and especially at the junction periphery.

Experiments

Devices structure.

The P + NN + diode is formed by multiple aluminum implantation at 300 °C on 6H-SiC n-type epilayer (thickness = 10 µm, doping level = 6´10 15 cm -3 ). The JTE (Junction Termination Extension) is created by five successive aluminum implantation at 300 °C. Some parameters of the junction termination (depth = 0.5 µm, doping level = 1´10 17 cm -3 , length ³ 50 µm) are determined by ISE software simulation (the ionization coefficients used are those given by Konstantinov et al. [1]) in order to obtain blocking voltage up to 1,7 kV. The samples were annealed at 1700 °C during 30 minutes [2], and metallized for anode and cathode ohmic contacting at the end. During contact annealing the metallization overflowed and covered the entire emitter region and a few microns of the JTE length. Two types of diodes are tested: D1 diodes with JTE non covered by metallization and D2 diodes with 30 µm of JTE length cover by anode contact (Fig. 1). A part of tested diodes underwent a RIE etching of 30 nm, thus decreasing the implanted dose of the JTE (Implanted dose = 6.4´10 12 cm -2 ). We will name those diodes D1E and D2E. The OBIC technique is often used to study electric field distribution in diodes structures [3]- [START_REF] Isoird | [END_REF]. It consists in measuring the variations of the current induced by a light beam focused in the surface of a device under reverse biases. If the energy of photons is larger than the band gap material electronhole pair are generated in the sample. When those carriers are generated in a space charge region (SCR) they are separated and speeded up by the electric field and then collected at contact where a current is detected. Fig. 2 shows experimental setup used to measure the variations of OBIC signal according to the spotlight position.

Results.

Electric characterizations.

The I-V characteristics and test of breakdown voltages were realized under DC conditions at room temperature in different ambient (air and silicon oil). The demonstrations of the breakdown were observed by means of a CCD camera and a video monitor.

The forward I-V characteristics present a good rectifying properties. Fig. 3a shows reverse I-V curves measured in air at 300 K. These curves present two distinct zones. In the first part (VR < 500 V) , the current increases as VR 1/2 , which corresponds to the generation/recombination of minority carriers in the space charge region. For VR ³ 500 V, the current rises quickly until breakdown (800 V < VBR < 1000 V). When the breakdown appears, electric arcs between the emitter edge and the anode contacting probe are observed. That illustrates a localized breakdown of the diode, induced by a high electric field at the P + N junction periphery. The electrical behavior observed during the tests in oil is almost identical. We find both zones of growth of the current and the breakdown occurs for the same range of voltage (Fig. 3b). We can note that the diodes, which are etched (D1E & D2E), present the same electrical characteristics in air and in oil. The breakdown voltage measured for D1E and D2E diodes is about 900 V. This value is not very different than this obtain 6 for not etched diodes. These results indicate that ambient and implanted dose of JTE have a poor influence on the premature breakdown of diodes. The experimental breakdown value is lower than that obtained by ISE simulation, which can be explained by too low doped JTE regions.

OBIC measurements.

Fig. 4 shows OBIC signal measured on D1 diodes along a diameter line at 351.1 nm corresponding to a light penetration length of 10 µm in 6H-SiC [7], with a spot diameter of 3.3 µm and optic power density of 1 W/cm 2 .

On Fig. 4a for Vr < 400 V, we observe rather a constant signal when a spot scans the JTE region (130-220 µm and 400-490 µm), which increases with the voltage due to the space charge region spreading. In the anode region (220-400 µm) we detect a photocurrent, which means that the metallization is partially transparent. When Vr = 500 V, a peak of photocurrent occurs at the JTE edge (490 µm) that indicates an equipotential line tightening. Note that the curves are not We describe now the OBIC measurements obtained from D2 diode on Fig. 5. First we recognize the same OBIC signal due to JTE region (110-190 µm and 390-490 µm) as for D1. A peak of OBIC signal emerges at one side of the metal (390 µm) when the bias reaches 400 V. Above 500 V, the photocurrent increases on both side of the metal contact. The JTE of the diodes D2 are covered by metal over a length of 30 µm. It is thus logical to detect a strong electric field at the anode periphery for weaker reverse bias than in the D1 diode. early depletion of the JTE. Although the total chemical dose implanted of not etched diode (1´10 13 cm -2 , as verified by SIMS analysis) to form the JTE is in good agreement with the acceptor dose determined by simulation, it clearly appears that this dose is not high enough to ensure a maximum protection. As we said previously certain diodes were etched. Therefore the acceptor dose of the JTE is 6.4´10 12 cm -2 . The breakdown value measured for etched diode (D1E & D2E) is weaker different than this obtain for non etched diode. These results mean that the acceptor dose has weak influence on breakdown voltage. The Fig. 6 shows the variation of VBR versus the acceptor dose of JTE. So we see in this curve that to obtain a breakdown voltage of about 1000 V, the necessary dose for the JTE is 2-3´10 12 cm -2 , which corresponds approximately to a rate of electrical activation of the aluminum of 30 %. However uncertainties on the parameters values used in the simulator such as the ionization coefficients or the doping and the thickness of the epilayer can involve errors on the obtained results. We have evaluated this errors to be in the 10 % range. So we can say that the electric activation is located in the range [20 % -40 %] and that it is much lower than that desired. The OBIC measurements performed on our planar SiC-6H bipolar diodes have indicated the JTE influence on electric field distribution and the place of breakdown. These results correlated with the electrical characterizations in different ambient tend to prove that the premature breakdown of our structures is due to a low rate of aluminum electrical activation.

Fig. 1 : 4 2. 2 .

 142 Fig. 1: P + NN + diodes.

Fig. 2 :

 2 Fig. 2: OBIC experimental setup.

Fig. 3 :

 3 Fig. 3: Reverse characteristics of bipolar diodes D1 and D2 at room temperature on different

-Fig. 4 :

 4 Fig. 4: OBIC measurement performed on D1 diode at l = 351.1 nm with Popt = 1 W/cm 2 (semi-

Fig. 5 :

 5 Fig. 5: OBIC measurement performed on D2 diode at l = 351.1 nm with Popt = 1 W/cm 2 (semi-

Fig. 6 :

 6 Fig. 6: Variations of the breakdown voltage versus acceptor dose of JTE obtain by ISE simulation

Acknowledgements.

This work was supported by GIRCEP group and Schneider Electric Research Center. We are also grateful to R. Nipoti from LAMEL Bologne for the realization of the ohmic contacts.


Stéphane Biondo
email: stephane.biondo@im2np.fr

Laurent Ottaviani
email: blaurent.ottaviani@im2np.fr

Mihai Lazar
email: cmihai.lazar@insa-lyon.fr

Dominique Planson
email: dominique.planson@insa-lyon.fr

Julian Duchaine
email: ejulian.duchaine@ion-beam-services.fr

V Le Borgne
email: fleborgne@emt.inrs.ca

M A El Khakani

Frédéric Milesi
email: hfrederic.milesi@cea.fr

Wilfried Vervisch
email: iwilfried.vervisch@im2np.fr

Olivier Palais
email: jolivier.palais@im2np.fr

M A El Khakani
email: elkhakani@emt.inrs.ca

F Torregrosa
email: kfrank.torregrosa@ion-beam-services.fr

4H-SiC P+N UV Photodiodes : A Comparison between Beam and Plasma Doping Processes

Keywords:

come   L'archive ouverte pluridisciplinaire

Introduction

During the past years there has been considerable interest in systems able to record very low light levels in the ultraviolet range in severe conditions of use. The advantage of Silicon Carbide (SiC) with respect to nitride alloys -the major wide band-gap semiconductor used today in industryrelies on three major points : a low residual doping for epitaxial layers (in the 10 14 cm -3 range and concentrations of residual defects/impurities at least one order of magnitude lower), a high thermal conductivity allowing high temperature operations, and a very good radiation hardness. It is then possible to use SiC for fabrication of devices capable to operate under extreme conditions. Photodetectors based on SiC allow to obtain good wavelength selectivity in the UV range, without any optical filters.

Experimental

The role of the p + emitter layer properties has been particularly studied in this paper. Among these properties, the doping and the thickness are thoroughly key parameters for controlling the device reliability. Photodetector simulations based on finite element method were performed, optimizing the design of the thin junctions for improvement of the light absorption and the carrier harvest. We also investigated the technological process giving rise to the dopant introduction into the SiC matrix. The comparison between standard ion implantation and pulsed-Plasma Immersion Ion Implantation (PIII) processes is expected to be fruitful, since PIII technology produced impressive results for Si solar cells in the UV range [1]. To our knowledge, PIII doping has never been carried out in SiC material. 4H-SiC n-type epilayers were either implanted with Aluminum by standard ion implantation at 27 keV, or with Boron by PULSION TM system (pulsed-plasma ion immersion) -B 2 H 6 at 8 kV, in order to produce p + -type layer thicknesses of 30 and 10 nm, respectively. The doses were adjusted for obtaining peak concentrations of few 10 19 cm -3 for Al (samples A) and few 10 20 cm -3 for B (samples B). This concentration discrepancy takes into account the difference of ionisation energies between Al and B dopants, and should give rise to similar values of the final hole concentrations in p + layers. Each sample was then annealed at 1700°C (samples A1, B1) or at 1650°C (samples A2, B2), aiming at analysing the influence of the annealing temperature on the device characteristics.

A prototype of furnace was used during this work (purchased from VEGATEC TM ), consisting in a vertical resistive reactor allied with a lift system. This allows to perfectly control the heating-up and the cooling-down rates, up to ~ 20°C/s. After Al implantations, we observed that a high heating-rate improved the sheet resistance whatever the annealing temperature, and preserved the surface roughness for annealing temperatures lower than 1700°C, which is crucial for thin implanted layers. The heating rate has indeed proven to be an important parameter for controlling the reverse current of the related diodes [2].

After thermal annealing, ohmic contacts were realized by sputtering with Ti/Al/Ni on p-type implanted layer (top contact) and Ni on n + -type substrate layer (bottom contact). The back contact on the substrate was annealed at 900°C and the contact on implanted layer was annealed at 800°C. Both contacts have been annealed during 2 min under Argon atmosphere. Finally, the UVphotodetector surface shape has a circle geometry with 250 µm-diameter. A window area allows to detect the UV photons. Fig. 1 shows the photodetector structure.

The optical simulations of photodetectors under the UV light have been realised by FDTD method (Finite Difference Time Domain), using the commercial software Sentaurus edited by Synopsys society [3]. Electromagnetic solver based on the FDTD method is used to calculate the electromagnetic field propagation inside UV-photodetector device.

Plasma Implantation in SiC

We propose to study the combination of PIII with a proper annealing, which should results in thin p + implanted layers (lower than 30 nm) particularly suitable for UV photon detection. PIII were performed on PULSION TM (Plasma ion implantation tool from the french company I.B.S.) using B 2 H 6 gas (see Fig. 2). Specificity of PULSION TM consists in using a pulsed DC polarization and a remote ICP plasma source allowing to work at low pressure (< 1x10 -3 mbar) with the use of low gas flow rate (< 10 sccm). This helps to minimize parasitic etching or deposition usually encountered on Plasma doping tools.

Ohmic contact

SiC epi

Substrate n + -Thickness @ 350 µm -Concentration @ 10 18 cm -3 Epitaxial layer n - -thickness @ 10 µm -Concentration @ 5x10 15 cm -3

Implantated layer

Fig. 1 Photodetector pn structure Fig. 2 PULSION TM set-up

A former study proved that, at a given energy, the plasma-process leads to a better surface morphology, a lower defect concentration and a thinner junction than a standard beam implantation process. This is accompanied with some dopant outdiffusion during the annealing, and a higher sheet resistance of the implanted layer [START_REF] Ottaviani | AIP Conf. Proc[END_REF].

Results and Discussion

Simulation

Figures 3 display the variation of the current density with the reverse bias (for an incident light wavelength at 200 nm), varying the p + -layer thickness (Fig. 3a) and the p + -layer concentration (Fig. 3b). In a general way, the current density increases with a thinner junction and a lower hole concentration. When the space charge region is closer to the surface, much more carriers undergoing the electric field are then harvested, leading to a better UV photodetector response. Fig. 3a Simulated current density vs reverse bias @ 200 nm, with a p + doping fixed at 5x10 19 cm -3 Fig. 3b Simulated current density vs reverse bias @ 200 nm, with a p + thickness fixed at 30 nm

Device Characteristics

The evolution of dark currents with reverse bias of the realised devices is shown in Fig. 4. Dark currents reveal to be lower with Al-implanted diodes (2 pA/cm 2 @ -5 V), whatever the annealing temperature. On the contrary, B-implanted diodes show higher forward currents than Al-implanted diodes (not shown here), revealing a "JBS-behaviour" due to in-diffusion of B atoms in the ternary compound formed within the top metal during the annealing at 800°C (see Ref. 5 for details). The SIMS profile of B atoms shows no diffusion during the annealing [START_REF] Biondo | [END_REF]. )

Reverse bias (V) thickness @ 0.01 µm thickness @ 0.03 µm thickness @ 0.05 µm thickness @ 0.10 µm thickness @ 0.30 µm thickness @ 0.40 µm thickness @ 0.50 µm

Fig. 4 FFig. 5

 45 Fig. 4 Dark currents of Al-and B-implanted photodiodes

Acknowledgement

The authors wish to thank Daniel Ehret (VEGATEC) for technical support, DGE (Direction Générale des Entreprises) for SiC-HT 2 project and ARCSIS for financial support.

Characteristics of the diodes have been then measured under light, with an incident wavelength of 365 nm. Fig. 5 displays the evolution of the "signal-to-noise" ratio with reverse bias. Alimplanted photodiodes reveal a ratio six times higher than B-implanted diodes. Fig. 6 gives the spectral response of these A1 photodiodes. As seen in Fig. 5, there is no influence of the reverse bias, which is a clear advantage if a fully autonomous system is required (for space applications).

The spectral responsivities of the four kinds of diodes are compared in Fig. 7. For a given dopant, the sensivity increases with the annealing temperature, which is surely related to a better recombination of the defects produced by the implantation process [6]. For a given annealing temperature, B-implanted diodes give rise to a higher signal than Al-implanted diodes. This can be due to a thinner junction and/or a lower hole concentration in the p + -layer, which lead to increase the current density of the device under light.

Summary

4H-SiC UV photodetectors were realised, based on implanted p + n junctions either by Al standard beam or by B plasma. Thanks to the optimised furnace for post-implantation annealings, the leakage current of the diodes remain as low as 2 pA/cm 2 . Boron plasma-implanted devices give rise to the best spectral responsivities. The behaviour of the diodes after irradiations is currently under study.


Mihai Lazar

Laurent Ottaviani

Marie-Laure L Locatelli

Christophe Raynaud

Dominique Planson

Erwan Morvan

Phillippe Godignon

Wolfgang Skorupa

Jean-Pierre P Chante

High Electrical Activation of Aluminium and Nitrogen Implanted in 6H-SiC at Room Temperature by RF Annealing

Keywords: Ion Implantation, Annealing, Recrystallization, SIMS, AFM, Electrical Activation

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Ion implantation in SiC, an indispensable technique for the p-n junction creation, due to very weak dopant diffusion coefficients, generates an important density of defects which involve high temperature annealing and particular conditions on ramp temperatures and environment partial pressures. High temperature and/or high energy ion implantations were used to reduce the material damage [1][2][3][4], however, these implant conditions remain not interesting in an immediate perspective of industrial applications. In this paper we present room temperature (RT) aluminium (Al) and nitrogen (N) ion implanted and rf-annealed samples with viable physico-chemical and electrical properties.

Experiment

Aluminium implantations were performed at RT in 6H-SiC n-type epitaxial layer samples purchased from Cree Research (3x10 15 cm -3 epitaxial doping) with energies ranging from 25 up to 300 keV and 100 to 400 keV with a total dose of 1.75x10 15 cm -2 and 1x10 15 cm -2 respectively. A thin aluminium mask (1500 Å) was used for the second implant series to avoid the channeling of Al ions, a major effect in 6H-SiC. Nitrogen was implanted in similar SiC samples at RT with energies ranging from 30 to 190 keV and a 1x10 13 cm -2 total dose, following an Al 350 keV high dose implantation in order to realise a n-p junction in surface.

All these samples were annealed at 1700°C during 30 mn with a preliminary heating slope of 40°C/s in argon atmosphere with a silicon carbide partial pressure, in a J.I.P.ELEC TM rf-induction heating furnace. The decreasing of the temperature at the end of the annealing is governed by thermal inertia.

SiC annealing in a rf induction furnace presents important advantages such as very high heating slope. This can allow the recrystallization of amorphised layers. Specially 4H and 6H-SiC samples need a very high heating slope in temperature to preserve the polytype from cubic inclusions, which may be generated during the solid phase epitaxy at too low temperature [5,6].

An inhomogeneous temperature in the heated wafer support is a drawback of the induction technique. Lateral and vertical thermal variations can induce SiC etching if appropriate environments and configurations rich in silicon and carbon are not provided. Silicon volatilizes at temperatures above 1300°C at one atmosphere pressure [3].

Dopant distributions before and after annealing have been investigated by Secondary Ion Mass Spectroscopy (SIMS) measurements, and have been compared to CNM-Monte Carlo simulations [7] of Al implantations in 6H-SiC. Damage induced in materials and recrystallization have been analyzed by Rutherford Backscattering Spectrometry in the Channeling mode (RBS/C). Concerning the surface its stoichiometry has been examined by X-Ray Photoelectron Spectroscopy (XPS) and its roughness by Atomic Force Microscopy (AFM). Finally electrical activation of dopants, i.e. incorporation of Al and N in substitutional SiC lattice sites, has been evaluated by sheet resistance measurements with a four point probe technique at RT.

Results

SIMS measurements

In order to examine the dopant profile distribution, CNM Monte-Carlo simulations were carried out as well as SIMS analyses before and after rf-annealing (Figure 1). The CNM Monte-Carlo simulation of Al ions implanted in 6H-SiC allows us to quantify the point defect generation and their effects in impurities distribution after annealing. We remark a good agreement between simulation and SIMS before annealing measurements Fig1. CNM Monte-Carlo simulation and SIMS analysis for the 100 to 400 keV Alimplanted layer SIMS profiles after annealing are not flat, Al dopant peaks are formed in high defect concentration zones due to an amorphous layer formation (Al peak at 3580 Å depth in Figure 1). We notice no dopant loosing after annealing.

RBS/Channeling measurements

Residual damage before and after annealing, SiC recrystallisation were evaluated by RBS/C technique. RBS spectra before and after annealing have been compared to a complete disoriented analyzed sample (amorphous) and a not implanted one (virgin). RBS/C analyses confirm the presence of an amorphous layer up to the surface in the implanted samples. Despite that, after a rf-annealing a satisfactory recrystallisation is found, the backscattering yield for the 1700°C/30 mn nearly coincides to the virgin one.

Fig2. RBS/C measurements for the 25 to 300keV Al implanted samples before and after annealing

To understand silicon carbide recrystallisation in RBS/C terms the heating slope during annealing was varied from 10°C per second to 40°C per second for samples implanted with the same dose or higher (18 times). RBS/C analyses showed that the layer remains amorphous in this latter case, and there is no influence of the heating slope on recrystallisation rate. It is possible, that there are at least two amorphous states depending on the implantation dose. Above a given deposited nuclear energy threshold, the layer may loose all bulk crystallinity information, and epitaxial recrystallisation may then become impossible even in optimized annealing conditions. Surface analyses A good surface stoichiometry is obtained by XPS after annealing. In Table 1 are presented the atomic ratio [Si/C] in surface and after thin layers Ar + sputtering. The thickness values correspond to a silica reference material. Two samples have been analyzed. Sample (A) placed in the suscepteur during annealing and sample (B) at the periphery. We remark that the surface of the sample (B) shows a carbonic layer deposited probably due to a lateral temperature gradient evaluated to be about 50°C. C 1s XPS energetic shifts are more related to a deposited layer rich in C-C/C-H bonds than a Si volatilization process. At 20 nm depth, sample B looks like sample A.

AFM surface measurements reveal a relatively high rms roughness (14nm) on annealed samples. Many samples have been analyzed to discern if either the recrystallization of the amorphized layer or either the rf-annealing is responsible for this roughness. For the virgin sample (Figure 3) a rms roughness of 0.31 nm before annealing denotes a good initial surface quality. After annealing we found very close values: 13.5 nm rms roughness for the virgin annealed and 14.4 nm rms roughness for the 100 to 400 keV Al implanted and annealed sample. Surface morphologies are nearly similar, the annealed virgin sample presents furrows much more parallel which is normal seeing the initial surface before the annealing of the implanted sample. These results are completely different, compared to an anneal in a resistive heating furnace with very similar time and temperature parameters (1700°C / 40 min) [8]. In these experiments indeed, the implantation step was responsible for an increase of the surface roughness.

Electrical measurements

Sheet resistance measurements by a RT four point probe technique on Al and N implanted and rfannealed samples prove a high electrical dopant activation for RT implanted samples, in terms of Al and N incorporation in SiC lattice sites (Si and C respectively). For Al-implanted samples a 19 kΩ/& sheet resistance at 300K has been found which corresponds to an electrical dopant activation

Conclusion

Al and N room temperature implantations were realized in 6H-SiC. Rf-annealing at 1700°C during 30 mn in a special silicon and carbon rich configuration performs satisfactory physico-chemical properties. Amorphous layers are recrystalized, dopants are not loosed during the heating process. If a good stoichiometry are obtained in surface a relatively high roughness is created at 1700°C. Reasonable electrical properties are attained, 50% activation for Al implanted dopants and 100% for N.

  Atomic force micrographs of (a) a virgin sample, (b) a virgin sample 1700°C/30 mn rf-annealed, (c) Al asimplanted sample and (d) Al implanted and 1700°C/30 mn rf-annealed sample. Edge length of images correspond to 5µm for non annealed samples and 19µm for annealed ones.

  Better results are found for Nimplanted samples (Figure4): 6.7 kΩ/& sheet resistance and 100% electrical dopant activation.

Fig4.

  Fig4. Sheet resistance measurements at 300K on a RT N-implanted layer

Table 1 Atomic

 1

	Depth (Å)	% C	% O	% Si	Si/C
	Sample A surface	44.4	16.0	39.6	0.89
	10	46.6	4.6	48.8	1.04
	70	48.3	2.1	49.6	1.03
	Sample B surface	84.3	3.6	12.1	0.14
	10	75.8	0.8	23.4	0.31
	70	56.2	1.6	42.2	0.75
	200	51.6	2.0	46.4	0.9

ratio determined by XPS for two samples annealed in the center (A) and the periphery of the suscepteur (B).

Acknowledgements

We would like to thank Mr André Plantier from the Institut of Nuclear Physics of Lyon for ion implantations, Mr Yves Monteil from LMI of Univ. C. Bernard Lyon I for AFM surface analysis, Mr Bruno Canut from DPM of Univ. C. Bernard Lyon I and Mr. M. Voelskow from Forschungzentrum Rossendorf for RBS/C measurements, Ms Christiane Dubois from LPM of INSA de Lyon for SIMS analysis, CIME of Grenoble for technological means and four point probe measurements, GIRCEP, Schneider Electric and Infineon for their interest in our investigations.