Léo Maczyta

Patrick Bouthemy

Olivier Le Meur

CNN-based temporal detection of motion saliency in videos

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Introduction

This paper is concerned with motion saliency in videos. Motion saliency information allows one to highlight objects undergoing separate, singular or unexpected motion in the video sequence. The specific problem addressed in this paper is the temporal detection of motion saliency in videos, i.e., determining throughout the video which frames contain motion saliency. This enables to recover time intervals for which motion saliency is present in the video.

To our knowledge, this problem has not been investigated so far, but it is crucial for numerous applications. It can help detecting obstacle irruption for mobile robotics or autonomous vehicles, raising alert for video-surveillance, triggering attention for video analysis, or highlighting relevant information for video summary. This frame-based classification acts as a pre-attention mechanism.

More specifically, our method can be viewed as a prerequisite to the computation of dynamic saliency maps. The existing methods extract salient moving objects, while implicitly assuming that the frame is dynamically salient. Our method precisely addresses the latter issue.

As aforementioned, temporal detection of motion saliency in videos is a classification problem. It consists in deciding for every frame of a video sequence whether it should be labelled as dynamically salient or not, that is, whether it contains elements whose local motion departs from their context. By context, we mean global (or dominant) motion. We will adopt to solve this classification problem the convolutional neural network (CNN) framework. Before classifying each frame of the video, we cancel the global motion usually due to the camera movement. We have investigated two ways of implementing it, based on image warping and on residual flow respectively. This leads to two main variants of our motion saliency detection approach.

The paper is organised as follows. Section 2 contains a brief review of motion saliency methods. Deep learning methods relevant to this problem are also discussed. In Section 3, we present the synthetic and real video datasets built to evaluate our classification scheme. In Section 4, we describe our approach and the two variants based on image warping and on residual flow respectively. Experimental results are reported in Section 5. Section 6 includes concluding comments.

Related work

To the best of our knowledge, there is no previous work that focuses on temporal motion saliency detection. Then, we will mainly browse contributions related to the computation of dynamic saliency maps in videos.

Research on dynamic saliency has mainly dealt with highlighting foreground objects moving in front of a static scene ( [START_REF] Tokmakov | Learning motion patterns in videos[END_REF][START_REF] Wang | Video salient object detection via fully convolutional networks[END_REF]). The scene may nevertheless be not fully static, but may comprise dynamic textures ( [START_REF] Crivelli | Motion textures: Modeling, classification, and segmentation using mixed-state markov random fields[END_REF]), such as flags in the wind or ripples on the water, that should not be considered as salient ([36]). Salient motion may be embedded in a global motion, when the camera is moving. A first category of methods compensate the camera motion in the image ( [START_REF] Le Meur | Predicting visual fixations on video based on low-level visual features[END_REF][START_REF] Huang | Video saliency map detection by dominant camera motion removal[END_REF]). A second class of approaches combines spatial and temporal information without first cancelling the camera motion ( [START_REF] Fang | Video saliency incorporating spatiotemporal cues and uncertainty weighting[END_REF][START_REF] Kim | Spatiotemporal saliency detection using textural contrast and its applications[END_REF][START_REF] Mahapatra | Coherency based spatiotemporal saliency detection for video object segmentation[END_REF][START_REF] Wang | Consistent video saliency using local gradient flow optimization and global refinement[END_REF]). [START_REF] Karimi | Spatio-temporal saliency detection using abstracted fully-connected graphical models[END_REF] use spatiotemporal cues and represent videos as spatio-temporal graphs with the aim of minimizing a global function. Related problems are the detection of anomalies in crowded scenes ( [START_REF] Pérez-Rúa | Detection and localization of anomalous motion in video sequences from local histograms of labeled affine flows[END_REF][START_REF] Xu | Detecting anomalous events in videos by learning deep representations of appearance and motion[END_REF]), and the use of eye-fixation maps for visual saliency ([4, 20, 28]).

Deep learning has greatly improved performances in many image processing tasks, notably for image classification ([16, 34, 9]). Video processing applications such as optical flow computation ( [START_REF] Ilg | Flownet 2.0: Evolution of optical flow estimation with deep networks[END_REF][START_REF] Sun | PWC-Net: Cnns for optical flow using pyramid, warping, and cost volume[END_REF]), moving object segmentation ( [START_REF] Tokmakov | Learning motion patterns in videos[END_REF]), automated description of videos ( [START_REF] Yao | Describing videos by exploiting temporal structure[END_REF]), or action recognition in videos ( [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF][START_REF] Sharma | Action recognition using visual attention[END_REF][START_REF] Li | Videolstm convolves, attends and flows for action recognition[END_REF]) showed that convolutional neural networks can also be successfully applied to videos. In particular, deep learning was recently applied to video saliency tasks. This is for instance the case for [START_REF] Wang | Video salient object detection via fully convolutional networks[END_REF], who estimate spatiotemporal saliency maps with motion and appearance information. They design an architecture comprising two successive CNNs, the first one dealing with spatial saliency and the second one refining the saliency map with temporal information. [START_REF] Le | Video salient object detection using spatiotemporal deep features[END_REF] use spatiotemporal deep features and define a spatiotemporal conditional random field to estimate saliency maps.

Most of the existing methods assume that motion saliency is attached to a foreground object moving in front of a (mostly) static scene. Then, appearance may play a key role and is consequently exploited. We take a more general definition of motion saliency. It involves all situations where local motion departs from its context with differences or not in appearance. Moreover, existing methods supply valued motion saliency maps. One could consider that they could recognize a dynamically non-salient frame, by providing a "practically" empty output. Yet, it would require designing an additional stage to reliably decide it. Anyway, we experienced that motion saliency maps are hallucinated by existing methods on non-salient frames. This shows that existing methods are not able to address the frame-based motion saliency detection problem.

Datasets

We start by describing the synthetic and real datasets used in our experiments. The dataset is indeed a key point for learning-based approaches, especially in the case of deep learning methods such as the ones we propose in this paper.

Synthetic dataset

Machine learning methods, and especially deep learning methods, require a training set large enough for successful learning and generalisation ( [START_REF] Sun | Revisiting unreasonable effectiveness of data in deep learning era[END_REF]). Recently it was demonstrated for computer vision applications ( [START_REF] Dosovitskiy | Flownet: Learning optical flow with convolutional networks[END_REF][START_REF] Mayer | What makes good synthetic training data for learning disparity and optical flow estimation?[END_REF]), that learning can be efficiently achieved on synthetic datasets. We have then built a synthetic dataset for a first training stage of the networks involved in our methods.

Each element of the synthetic dataset consists of a frame pair. The second frame of the pair is obtained by applying to the first frame a parametric motion model. We take an affine motion model. Motion saliency is attached to added patches taken from other images and undergoing a different affine motion. Images of PascalVOC 2012 ( [START_REF] Everingham | The PASCAL Visual Object Classes Challenge 2012[END_REF]) were used to generate this dataset. Samples are provided in Fig. 1. Frames are generated with a probability of 0.5 for absence of motion saliency, 0.25 for presence of one salient moving patch, and 0.25 for presence of two salient moving patches. Added patches have a limited size, of approximately 0.5% to 1.5% of the frame. We deliberately chose to include small-size patches to produce motion saliency examples hard to detect.

By generating salient examples in this manner, the risk could be that the network manages to detect salient frames thanks to the appearance of the added patches generally different from the content of the reference frame. To avoid this, non salient frames can also contain up to two added patches coming from

Dataset of real videos

A dataset with real videos is necessary for fine-tuning the networks and to assess the motion saliency classification methods. Since the proposed methods will have to handle videos with a mobile camera, the camera should be moving for a significant part of the dataset. Moreover, the ground truth has to supply the presence of motion saliency or not at the frame level. The dataset constructed by [START_REF] Bideau | A detailed rubric for motion segmentation[END_REF] meets these requirements. It gathers FBMS-59 ( [START_REF] Ochs | Segmentation of moving objects by long term video analysis[END_REF]), Complex Background ( [START_REF] Narayana | Coherent motion segmentation in moving camera videos using optical flow orientations[END_REF]) and Camouflaged Animals ( [START_REF] Bideau | Learned-Miller. It's moving! a probabilistic model for causal motion segmentation in moving camera videos[END_REF]), all of them being specifically re-annotated for the problem of moving object segmentation. This dataset contains difficult examples, in particular the videos of Camouflaged Animals for which motion information strongly prevails in the perception of the salient moving animals. We label a frame as dynamically salient in the ground truth, if it contains at least one independently moving object in the ground truth of the original dataset.

Since our objective is to detect temporal intervals of motion saliency in videos, non salient frames are required to serve as negative examples. Non salient frames are rare in the dataset of [START_REF] Bideau | A detailed rubric for motion segmentation[END_REF]. Consequently, we acquired 71 additional videos with no salient frames, i.e., depicting static scenes, but acquired with a mobile camera. These videos depict indoor, urban and natural scenes.

The ground-truth is by construction available for all the frames of the 71 additional non-salient videos.

The final dataset includes 144 videos, and is split in a training set of 94 videos, a validation set of 13 videos and a test set of 37 videos, for a total of 3451 labelled frames. The three sets contain approximately the same amount of salient and non salient frames. During training, data augmentation with resizing, cropping, temporal inversion and flipping around a vertical axis, was applied. The batches used to train our networks are built so that salient and non salient frames are correctly balanced. Samples are displayed in Fig. 2. Our overall approach is summarized in Fig. 3. Its core module is the frame classification, which will be achieved with a convolutional neural network (Section 4.1). We have investigated two ways to implement the global motion compensation: image warping (Section 4.2), and residual flow (Section 4.3).

We assume that the dominant motion (or global motion) can be represented by a single affine motion model. We have considered two parametric dominant motion estimation algorithms, Motion2D and DeepDOM, described in Section 4.4.

In addition, we have designed two baselines for comparison purpose, since there are no existing methods available for temporal motion saliency detection. The first baseline merely thresholds the average residual flow magnitude (Section 4.5). It essentially allows us to assess the difficulty of the problem. The second baseline is defined in Section 4.6. It leverages the two-stream network introduced in [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF].

Frame classification on motion saliency

Due to the clear superiority of CNN-based methods in any image classification problem to date, we adopt a CNN framework for the frame classification issue attached to the temporal detection of motion saliency. The input will be specified for each method of temporal motion saliency detection. The input will be formatted as 240x240 frames. We defined the structure of the CNN for classification (Fig. 4) from preliminary experiments on the synthetic dataset. We conclude from these experiments that a too deep network led to overfitting. Therefore, only three convolutional layers are kept, with 7x7 kernels to have large enough receptive fields. The convolutional layers involve respectively 64, 96 and 128 features maps, with a stride of 2. This architecture also comprises max pooling with a stride of 2, batch normalization, the ReLU non-linearity and dropout.

Our objective is to build a flexible framework. As a consequence, we keep the same architecture for the classification in all the motion saliency detection methods, whatever the input data. With this specification, we can also draw a fair comparison of the methods. The CNN architecture was trained for each method with the cross-entropy loss.

The network only supplies the probability of the frame to be dynamically salient. A frame is then classified as dynamically salient if this probability is greater than 0.5, and non salient otherwise. Notwithstanding, we are not facing a threshold setting issue. Comparing to 0.5 is equivalent to taking the maximum of the two probabilities, when their sum is equal to one. We first designed a network with two output yielding probabilities for the dynamically salient and non-salient classes respectively. We experimentally noticed that the sum of the two probabilities was very close to one, without formally imposing this constraint in the network. More precisely, the mean of the absolute difference between 1 and the sum, on the overall test set, was 3.7 10 -5 , and its maximum value amounts to 4.7 10 -4 , knowing that the test set is well balanced between the two classes. Then, for the sake of simplicity and efficiency, we built a network with one single output.

Motion saliency detection based on image warping

The first way to cancel the dominant motion is to warp the second image of the pair onto the first one. The method will then rely on the CNN to extract relevant features from the aligned frames to infer whether motion saliency is present or not, i.e., the frames are partly or fully aligned.

The first step of this method consists in warping the second frame onto the first one with the estimated affine motion model. Then, the frame classification is performed with the convolutional neural network introduced in Section 4.1. The two colour images, which are the first input image and the second warped image, are concatenated into a 6-channel input.

Depending on the dominant motion estimation algorithm used, this method will be called WS-Motion2D or WS-DeepDOM, with WS standing for Warping-

Motion saliency detection based on the residual flow

Our second motion saliency detection method fully acknowledges the fact that motion saliency is first and foremost related to motion. Instead of using colour frames as input, we directly exploit information related to motion. This allows us to be explicitly agnostic on appearance. We compute the residual optical flow, by subtracting the affine flow ω θ , given by the estimated dominant motion model of parameters θ, to the computed optical flow field ω:

∀p ∈ Ω, ω res (p) = ω(p) -ω θ (p) ( 1
)
where Ω is the image grid. The residual flow will serve as input of the CNN classifier of Fig. 4. This method is summarized in Fig. 6. The two components of the residual flow are the two channels of the input of the classifier. A residual flow close to a zero field over the whole image means a non salient frame. The optical flow is computed with FlowNet2.0 ( [START_REF] Ilg | Flownet 2.0: Evolution of optical flow estimation with deep networks[END_REF]). FlowNet2.0 was chosen since it is real time, while delivering good performance. The speed of the optical flow algorithm is indeed critical, as optical flow is computed during training for each pair of every batch. Also, expected use cases of motion saliency detection methods are likely to require a real-time execution. Depending on the motion estimation algorithm used, this method will be denoted RFS-Motion2D or RFS-DeepDOM, with RFS standing for Residual Flow Saliency.

Parametric estimation of the dominant motion

Most of the time, the image motion induced by the camera forms the dominant motion in the frame. This dominant motion corresponds to the apparent motion of static elements in the scene, which usually occupy the main part of the frame. In case of a shallow scene, i.e., depth and orientation variations in the static scene are small compared to the distance to the camera, a unique 2D parametric motion model, such as an affine model, correctly approximates the dominant motion. We adopt this simple but efficient approach. Results reported in Section 5 will show that it generalises well even for non shallow scenes in practice. Let us note that for a close-up on a moving object, the dominant motion becomes precisely the motion of this object. The temporal motion saliency detection is still valid, since we are interested in detecting local motion departing from the global one. For every pixel p ∈ Ω with p = (x, y), the flow field given by the parametric motion model can be written as:

ω θ (p) = a 1 + a 2 x + a 3 y a 4 + a 5 x + a 6 y (2)
where θ = (a 1 , ..., a 6 ) is the vector of the model parameters. We used two methods to estimate the affine motion model. We first resorted to the classical robust multi-resolution algorithm Motion2D ( [START_REF] Odobez | Robust multiresolution estimation of parametric motion models[END_REF]) to estimate the dominant affine motion model between two frames. It is real-time and it has proven its efficiency in many applications.

As an alternative, we have applied the network proposed in ( [START_REF] Rocco | Convolutional neural network architecture for geometric matching[END_REF]), initially designed to estimate geometric transformations between two images, which can be acquired from very distant viewpoints. The objects to match may even be different instances of the same object class. In our case, the two input images are far closer and the displacements are smaller, but the frames may involve outliers, i.e., independently moving objects. The architecture is summarized in Fig. 7. It was trained separately from the saliency detection methods, with the synthetic dataset described in Section 3.1. The convolutional neural network after the correlation operation is composed of two successive convolutional layers with respectively 7x7 and 5x5 kernels, that are followed by a fully connected layer. This network is called DeepDOM, for Deep Dominant Motion estimation. The loss function used to train this network is similar to the one used in [START_REF] Rocco | Convolutional neural network architecture for geometric matching[END_REF]. A grid G of nodes q is deformed by the estimated global motion of parameters θ and by the ground truth θ GT . The loss function ( θ) compares the two sets of grid displacements as follows:

( θ) = 1 N q∈G ||ω θ GT (q) -ω θ (q)|| 2 2 .
(3)

Basic baseline

To assess the difficulty of the task, we defined a very simple baseline based on the residual flow. We compute the following criterion to decide whether the frame t is dynamically salient:

Φ(t) ≥ λ , with Φ(t) = 1 |Ω| p∈Ω ω(p, t) -ω θ(t) (p) 2 ( 4
)
where ω is the optical flow, ω θ is the flow field corresponding to the dominant motion model of estimated parameters θ, and Ω is the image domain. Motion2D is used to estimate the dominant motion, and FlowNet2.0 to compute the optical flow.

Φ(t) characterizes the amount of residual motion in the frame t after cancelling the global motion. We threshold Φ(t) to classify the frame t as dynamically salient or not. To correctly set the threshold value λ, we compute the empirical distribution of Φ(t) on the frames of the validation set of the real video dataset. Then, an exponential law is fit to it. A p-value test with a probability of false alarm of 5% is applied to set the threshold λ.

Two-stream network as a baseline

Due to the lack of existing methods to compare with, we designed a second more challenging baseline, which is a direct application of the well-known two-stream network introduced in ( [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF]). The two-stream network involves two CNNs in parallel, one for the spatial stream, and one for the temporal stream. The twostream network provided convincing results for the action recognition task. It was also exploited in other works, such as in [START_REF] Tesfaldet | Two-stream convolutional networks for dynamic texture synthesis[END_REF] for dynamic texture synthesis and for the prediction of eye-fixation maps in [START_REF] Bak | Spatio-temporal saliency networks for dynamic saliency prediction[END_REF]. We can expect that it is a good candidate for temporal motion saliency detection as well. However, we need to adapt it for this new task. We have to produce a prediction for each frame of a video, and not one prediction for the whole video. Regarding the spatial stream, we concatenate the two considered frames of the video. Accordingly, as input of the temporal stream, we take the optical flow field computed between the two frames only. In this version of the two-stream network, we still use the classification CNN of Fig. 4 for both streams, and FlowNet2.0 to compute the optical flow. The resulting network is summarized in Fig. 8.

Experimental results

Experimental setting

We use the Caffe library ( [START_REF] Jia | Caffe: Convolutional architecture for fast feature embedding[END_REF]) to implement the networks presented in Section 4. The optimization was achieved with the Adam method ( [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF]) with the parameters proposed by the authors. The runtime for the processing of a batch during the learning stage (prediction and back-propagation), with a GPU Tesla M40 and a 2.9 GHz processor is respectively of 1.4 sec, 1.8 sec, 0.7 sec and 1.2 sec for WS-DeepDOM, WS-Motion2D, RFS-DeepDOM and RFS-Motion2D. The batch size consists of 32, 32, 8 and 12 elements respectively. In the test stage, the prediction for one frame is performed in respectively 20.0, 15.2, 10.4 and 9.5 fps.

Comparison of the dominant motion estimation methods

The accuracy of the dominant motion estimation methods is likely to play an important role in the performance of the temporal motion saliency detection methods. We evaluated them on the synthetic dataset, which contains 2000 elements for which the ground truth is available by construction. With Deep-DOM, we obtained a mean error for the estimation of the dominant motion of 0.20 pixels with a standard deviation of 0.08. With Motion2D, the mean error is of 0.03 pixels and the standard deviation is of 0.41. For the synthetic dataset, Motion2D provides an estimation of the motion closer to the ground truth. Yet, the accuracy obtained by DeepDOM remains reasonable.

Choice of the time step

An important aspect is the choice of the time step ∆t between the two frames at test time. A time step of 1 means that we consider two successive frames of the video, a time step of 2, frames at t -2 and t, etc. We can expect that more distant frames will make the highlight of independently moving objects with small motion magnitude easier. On the other hand, the parametric estimation of the dominant motion is supposed to be more precise on temporally closer frames. Table 1 illustrates this behaviour for the RFS-Motion2D method. To find the best trade-off, time step was set to 1 during training, and the validation set with real videos is used to select the best time step for every method. Each method has thus the most appropriate time step for testing. It will also make the comparison fairer.

Experimental evaluation and comparison

Evaluation on the synthetic dataset

All the motion saliency detection methods were trained first on the synthetic database introduced in Section 3.1. The evaluation on the synthetic validation set showed that for all the methods, the accuracy was higher than 98%. Such a good performance can be explained by the fact that the synthetic dataset is ideal, as it displays strictly affine motions.

Evaluation on the real video dataset

Table 2 collects the comparative results. First of all, the simple baseline relying on criterion (4) based on the residual flow yields only a 54.4% accuracy, which shows that the problem is not trivial. The two-stream network has an accuracy of 80.9%, which is better, but leaves room for improvement. Let us note that the spatial and temporal streams, when used separately to make the prediction, do not provide equivalent results. The temporal stream performs better than the spatial stream by a large margin. This confirms that for the temporal motion saliency detection task, explicit motion information is the key input. In contrast to the action classification task investigated in ( [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF]), no improvement was obtained by combining the two streams. Table 2 shows that the best method is RFS-Motion2D, which is based on the residual flow. It reaches an overall accuracy of 87.5%. Globally, we draw the same conclusion than for the two-stream baseline, that is, for a given dominant motion estimation algorithm, methods which take flow as input perform better that methods that take images as input. Samples of results are displayed in Fig. 9 for visual assessment. In the processed videos, the static scene is not always shallow, and moving objects may be not easily visible. However, the classification results remain convincing as illustrated in the top rows of Fig. 9. Difficult cases involving static objects in the foreground as trees or walls, dynamic textures as a flowing river, or camouflaged moving objects as the snail, are correctly classified. The bottom row of Fig. 9 contains failure cases for frames involving wind in the bushes, or small moving objects (respectively car, dog, scorpion) partially hidden.

Impact of the quality of the dominant motion estimation

Using DeepDOM did not improve the results compared to the classical method Motion2D. This suggests that Motion2D still outperforms DeepDOM for real images. Methods WS and RFS are not equally affected by a lower accuracy of the dominant motion estimation method on real videos. Table 2 shows that the performance decrease is 2.9% for RFS and 8.7% for WS, when the training is performed on the synthetic dataset only. This suggests that RFS is more robust to a less precise motion estimation method. Intuitively, WS really needs a correct cancellation of the camera motion when comparing registered pixels at the same location to detect motion saliency. In contrast, optical flow contains information directly exploitable, and the camera motion compensation acts as a "denoising" step.

Choice of the parametric motion model

All the reported experiments involve an affine motion model. To evaluate how the chosen motion model impacts the classification performance, we ran WS-Motion2D with two different motion models: the affine one and the 8-parameter quadratic one. The latter consists of a polynomial of degree 2 for the two components of the velocity vector for a total of 8 free parameters. It accounts for the 2D projected motion of a 3D rigid motion of a planar scene, similarily to the homography for geometric transformation. The WS-Motion2D method has been chosen for this comparative evaluation, since it is more affected by the quality of the global motion estimation as mentioned above. The classification CNN is not trained again. Results reported at the bottom of Table 2 (referred as WS-Motion2D affine and quadratic) show that modifying the motion model at test time has almost no impact on the performance.

Impact of fine-tuning

By comparing results collected in Table 2 and Table 3, which reports the performance of the methods trained only on the synthetic dataset, we notice that further training on real videos always improves the performance. WS-Motion2D has already good performance with training on synthetic data only, but the other methods really benefit from the fine-tuning on real videos.

Temporal evaluation

The temporal behaviour of the four variants is illustrated in Fig. 10 colour of the frame border, resp. orange or blue, designates the prediction, resp. dynamic saliency or non saliency. The green (resp. red) square at the bottom right indicates that the prediction is correct (resp. wrong). The video shows that non saliency is correctly predicted even when the shallow scene assumption is not valid, with for instance walls, trees or pillars in the foreground. The video includes the camouflaged snail case which is successfully handled.

Additional experiments

We applied RFS-Motion2D to the DAVIS2016 dataset ( [START_REF] Perazzi | A benchmark dataset and evaluation methodology for video object segmentation[END_REF]), which includes 50 videos with 3455 annotated frames. It was initially built for the video object segmentation task. The objects of interest are foreground moving objects. All the frames should be labeled as dynamically salient. We run RFS-Motion2D without any fine-tuning on the DAVIS2016 dataset. We obtained an overall correct classification rate of 93.3%, which is significantly better than the one obtained for our real dataset (79.7% for salient frames, see Table 2). Moving objects in our dataset usually exhibit a smaller motion magnitude than those of the DAVIS2016 dataset. Our dataset also includes the challenging Camouflaged Animals samples.

In addition, we conducted an experiment regarding the claim made at the end of Section 2. We applied the method of [START_REF] Wang | Video salient object detection via fully convolutional networks[END_REF] on the non-salient videos, using the code made available by the authors. As expected, the method supplied nonempty motion saliency maps in every frame, sometimes involving large nonsalient areas. To be fair enough, we added a decision step for an effective classification, and we got a correct classification rate of non-salient frames of 58%. In contrast, our method reached a rate of 98% (Table 2). This shows that existing methods are not able to handle the problem we solve.

Conclusion

We have formulated the problem of temporal motion saliency detection in videos. We proposed two methods involving camera motion compensation and CNNbased classification. They were favourably compared to a baseline exploiting the two-stream network. A synthetic dataset has been constructed and an existing real dataset has been extended. The best method (RFS-Motion2D) reaches an overall accuracy of 87.5% on our real dataset, and even 93.3% on the DAVIS2016 dataset. It takes advantage of an explicit motion information given by the residual flow. Future work will try to improve the dominant motion estimation for complex scenes, and to investigate the introduction of temporal regularisation in the frame-based classification.

Figure 1 :

 1 Figure 1: Samples of the synthetic video dataset. (a) The top frame is not salient while the bottom frame is salient. Both include added patches cropped from another image, but only the one in the second example undergoes an independent motion. (b) The corresponding motion fields are represented with the colour code depicted in c). The optical flow is globally smooth in the top row, while it includes an outlier patch in the bottom row. (c) Colour code for the flow field represented below.

Figure 2 :

 2 Figure 2: Four samples of the real dataset. The two first examples involve non salient frames. The two last ones comprise dynamically salient frames (respectively, including a moving cat, and a (small) moving car behind the parked cars). Four consecutive frames are displayed for each example.

Figure 3 :

 3 Figure 3: Our overall motion saliency detection framework

Figure 4 :

 4 Figure 4: CNN for motion saliency classification ('str' stands for stride). The number of channels are mentioned at the relevant places. The input is either the 2-channel residual flow or the 6-channel concatenation of the two RGB images. The final output is the probabillity of motion saliency, computed with a sigmoid.

Figure 5 :

 5 Figure 5: Motion saliency detection based on frame warping

Figure 6 :

 6 Figure 6: Motion saliency classification based on the residual flow

Figure 7 :

 7 Figure 7: DeepDOM network leveraging the one from [29] for the parametric estimation of the dominant motion.

Figure 8 :

 8 Figure 8: Two-stream network for motion saliency classification.

Figure 9 :

 9 Figure 9: Classification examples for RFS-Motion2D. Top left rows and top right rows show six frames properly classified as respectively non dynamically salient and dynamically salient. The bottom row displays four failure cases.The first frame is wrongly classified as dynamically salient due to wind in the bushes; the red circle surrounds the salient moving object in the three next frames wrongly classified as non salient.

Figure 10 :

 10 Figure 10: Comparative timelines of the frame classification on 12 real videos of different length (orange stands for dynamically salient, blue for non-salient).

Table 1 :

 1 with timeline plots. The 12 real video clips respectively depict moving camel, cars (twice), cat, tractor, giraffe and scorpion for the seven dynamically salient clips, field, river, countryside, campus and indoors for the five non salient ones. Nine of them are represented in Fig.9. All methods supply more stable results on non salient videos than on dynamically salient ones. The seventh clip in the row is a very difficult example (the scorpion one) of the Camouflaged Animals dataset. RFS methods are able to partly detect motion saliency in this video, whereas WS methods fail, demonstrating that motion information is the key clue and appearance may be useless.Results obtained with RFS-Motion2D for a subset of the test set, and concatenated in a single video, are provided in the supplementary material. The Rates of correct classification in percentage for RFS-Motion2D on the real validation set for several time steps ∆t.

	Time step ∆t 1	2	3	4	5	6	7	8	9
	Accuracy	84.9 89 88.3 88.9 89.6 88.9 88.9 89.2 88.5
	Method		Time Overall Salient Non salient
				step			frames	frames
	Baseline Φ		4		54.4	64.6		42.8
	Two-stream network	1		80.9	64.3		99.7
	Temporal stream only	2		83.2	71.4		96.7
	Spatial stream only		3		72.6	50.4		98.0
	WS-DeepDOM		2		76.5	59.4		96.2
	RFS-Motion2D		5		87.5	79.7		96.4
	RFS-DeepDOM		3		84.6	73.0		98.0
	WS-Motion2D affine	6		85.2	78.4		93.0
	WS-Motion2D quadratic	6		85.2	77.9		93.5

Table 2 :

 2 Rates of correct classification on the test set with real videos. Best performance in bold, second best underlined.

	Method	Overall Salient Non salient
			frames	frames
	WS-Motion2D	80.9	76.8	85.7
	WS-DeepDOM	67.3	62.3	73.1
	RFS-Motion2D	76.0	62.2	91.8
	RFS-DeepDOM	69.7	44.1	99.0

Table 3 :

 3 Rates of correct classification on the test set with real videos, where methods are trained only on the synthetic dataset.

Acknowledgements

This work was supported in part by the DGA and the Région Bretagne through co-funding of Léo Maczyta's PhD thesis.


Michel Tabarant

Laurence Latu-Romain

Yves Wouters

Quentin Bignon

Frantz Martin

Quentin Auzoux

Frédéric Miserque

Corrosion of titanium alloys in primary water of nuclear pressurised water reactor

Corrosion of titanium alloys in primary water of nuclear pressurised water reactor

INTRODUCTION

Titanium alloys could be good candidates for nuclear Pressurised Water Reactor (PWR) primary circuit structure components because of their low neutron activation and their good mechanical properties. However, corrosion resistance of titanium alloys in primary water conditions is poorly known High temperature (T > 500 °C) oxidation of titanium alloys in oxygen, in dry air or in water vapour has been extensively studied mainly for aeronautics applications [START_REF] Gurrappa | An oxidation model for predicting the life of titanium alloy components in gas turbine engines[END_REF]- [START_REF] Guleryuz | Oxidation of Ti-6Al-4V alloy[END_REF]. Titanium alloys exposure to such environment may involve the formation of different titanium oxides (TiO 2 , Ti 3 O 5 , Ti 2 O 3 and TiO). Nevertheless, the most deleterious phenomenon occurring at temperatures above 800 °C is the dissolution of oxygen in the bulk metal (-case) leading to the modification of mechanical properties of titanium alloys [START_REF] Gurrappa | An oxidation model for predicting the life of titanium alloy components in gas turbine engines[END_REF], [START_REF] Wouters | Thermal oxidation of titanium by water vapour[END_REF]. Kofstad et al. have determined that more than 80 % of reacted oxygen was dissolved in titanium exposed 30 min at 900 °C to oxygen atmosphere [START_REF] Kofstad | Oxidation of titanium in the temperature range 800-1200°C[END_REF]. This process is due to high oxygen solubility in titanium (33 at %) and high diffusion rate of oxygen at elevated temperature.

Few studies have investigated the corrosion behaviour of titanium alloys in liquid water environment at intermediate temperature [START_REF] Kaneda | General corrosion properties of titanium based alloys for the fuel claddings in the supercritical water-cooled reactor[END_REF]- [START_REF] Gorynin | Titanium alloys for marine application[END_REF]. Kaneda et al. have studied corrosion resistance of different / and  titanium alloys exposed 500 h to supercritical water (25 MPa,. Very thin oxide film (less than 1 µm) covered by dispersed particles were observed at 290 °C for both / and  alloys. Oxides compositions were not investigated at this temperature. At 550 °C, film thicknesses observed after 500 h were around 5 µm and 20 µm for  and / alloys, respectively. Outer layers of  alloys oxide were enriched in Cr, Mo and Zr alloying elements. The authors attribute the better general corrosion resistance of  alloys as compared to / alloys at 550 °C in terms of mass uptake and oxide layer thickness to alloying elements. However, the structure and the nature of oxides layers as well as corrosion mechanisms taking place in water environment at temperatures close to 300 °C are still undetermined.

The present study aims at determining the corrosion mechanisms of three titanium alloys (, / and -metastable) in PWR primary water environment (T = 300 °C, P = 150 bar, [H 2 ] = 25 cm 3 NTP (Normal Temperature and Pressure).water kg -1 , theoretical pH 300°C = 7.5). The influence of both chemical compositions and microstructures of alloys on corrosion mechanisms was investigated. Specimens were exposed for durations ranging from 24 h to 3499 h in a corrosion loop. The oxide layers grown on the specimen were analysed by X-ray diffraction (XRD), Raman spectroscopy, Energy Dispersive X-ray Spectroscopy (EDS) and Transmission Electron Microscopy (TEM) ASTAR. The growth mechanisms of the oxide layer were studied by Scanning Electron Microscopy (SEM), Glow Discharge -Optical Emission Spectroscopy (GD-OES), X-ray Photoelectron Spectroscopy (XPS) and Photoelectrochemistry (PEC). Finally, the question of the existence of an -case was then assessed by nanoindentation. Corrosion kinetics measurements and modelling will be published in a separate paper.

MATERIALS AND PROCEDURES

Materials

Three titanium alloys representative of different metallurgical classes have been studied: T40, TA6V and Ti-10-2-3. T40 is a single phase  alloy also called commercially pure titanium (ASTM grade 2), TA6V or Ti-6Al-4V (ASTM grade 5) is a two-phase / titanium alloy and Ti10-2-3 or Ti-10V-2Fe-3Al is a two-phase  metastable alloy. Their chemical compositions are given in Table 1. The three alloys were supplied by TIMET Savoie as billets in mill-annealed state. Final heat treatment temperatures were 675 °C, 730 °C and 760 °C for T40, TA6V and Ti10-2-3 respectively. The microstructures of the three alloys are given in Figure 1. T40 showed an equiaxed grain microstructure. Average grain size was 60 µm. TA6V and Ti10-2-3 had a finer bimodal microstructure. Electron Backscatter Diffraction (EBSD) analysis were carried out to determine the volumetric α/β phase ratios. TA6V and Ti10-2-3 contained respectively 4 % and 38 % by volume of  phase. Specimens' length, width and thickness were 20 mm, 10 mm and 1 mm, respectively, and machined so that length and thickness of specimens were along radial and longitudinal directions of billets, respectively. EBSD analysis showed that c axis of the  hexagonal unit cell were mainly oriented along the length and the width for T40 and Ti10-2-3 specimens and mainly along the thickness for TA6V specimens. Specimens were SiC paper grounded from grid 1200 to grid 2400, diamond paste (from granulometry 3 µm to 1 µm) and colloidal silica suspensions (pH = 9) polished. Gold deposits of around 1 nm thick were performed with a Quorum Q 150 R ES metallizer (I = 10 mA, t = 60 s) on dedicated specimens before exposure to primary water to mark initial surface position and determine the growth direction of the oxide scale.

Materials exposure to primary water

Characterisation techniques

Electrochemical nickel plating were performed on samples before cross sections preparation to prevent oxide scale decohesion. The samples surfaces and cross sections were observed with an Ultra 55 Zeiss Field Emission Gun Scanning Electron Microscopy (SEM) at 5 kV. Oxides diffraction patterns were obtained by analysis of sample surface of 7 x 7 mm² at grazing incidence (0.8°) with a PANalytical X'Pert PRO MPD X-Ray Diffractometer (XRD). Oxides were also analysed with a Horiba XploRA™PLUS Raman spectroscopy with a laser wavelength of 532 nm, a XFlash Detector 4010 Bruker Energy Dispersive X-ray Spectroscopy (EDS) at 20 kV, a Thermofisher Escalab 250 xi X-ray Photoelectron Spectroscopy (XPS) from Al k ray (1486.6 eV) and a Profiler 2 Horiba Scientific Glow Discharge -Optical Emission Spectroscopy (GD-OES). XPS was equipped with an Ar + ion sputtering gun for elementary concentration profiles realisation.

The precession-assisted crystal orientation mapping technique, ASTAR, which is a TEM-based diffraction spot recognition technique [START_REF] Rauch | Automated crystal orientation and phase mapping in TEM[END_REF] was used to acquire oxide phase maps. It was realised on a Transmission Electron Microscope at 200 keV (FEG-TEM, Jeol 2100 F). Transversal cross-sections thin foils were previously prepared with a dual-focused ion beam scanning electron microscope (FIB-SEM, Zeiss Cross Beam NVision 40).

Semiconducting properties of oxide scales were determined by Photoelectrochemistry (PEC). PEC setup was made of an electrochemical glass cell with three electrodes. The working electrode was the oxidised titanium sample to be studied, the reference electrode was a Mercury Sulfate Electrode (MSE) and the counter electrode was made of a platinum sheet. The electrolyte used was Na 2 SO 4 at the concentration of 0.1 mol.L -1 . Monochromatic light was provided by a xenon lamp connected to a grating monochromator. The PEC cell was connected to a potentiostat to impose potential conditions. The photocurrent was measured using a lock-in amplifier and a mechanical light chopper. PEC spectra presented in this study were recorded at a light frequency of 15 Hz, at potentials ranging from -420 mV/MSE to +280 mV/MSE and at energies ranging from 2.5 eV to 5 eV.

The potential oxygen enrichment in the alloys under the oxide scale was investigated by nanoindentation (Micro Materials NanoTest TM NTX). Evolution of hardness with depth under the oxide layer was studied thanks to several indent sets realised on samples cross sections. In a set, indents were spaced by 1 µm in sample depth direction and 10 µm laterally. Indent used were Berkovich type indent. The maximum applied force was around 9 mN and indentation depths and widths were around 300 nm and 1 µm, respectively. Polishing procedure of samples cross sections before nanoindentation tests was exactly the same as the one used for all samples before corrosion tests.

RESULTS

Characterisation of the oxide scales

XRD diffractrograms at grazing incidence of T40, TA6V and Ti10-2-3 after 434 h exposure to primary water are shown in Figure 2. The XRD analysis depth is of the order of few micrometers and the detection limit is around 1 %. Figure 2 indicates that the oxides of the three alloys formed during exposure were composed of TiO 2 anatase with tetragonal crystal system and FeTiO 3 ilmenite with trigonal crystal system. TiO 2 rutile with tetragonal crystal system is detected in T40 oxide only (2 = 27.5 °, d 110 = 3.247 Å). Ti- phase from the metallic part of the three materials as well as Ti- phase of TA6V and Ti10-2-3 were also identified. The peak located at 43.3 ° was attributed to impurities of metallic copper with cubic crystal system which may come from the stainless steel loop. GD-OES profiles carried out on all alloys after 434 h exposure to primary water are shown in Figure 3. In all cases, an oxygen enrichment was evidenced at the surface down to around 0.5 µm depth. For TA6V and Ti10-2-3, no aluminium nor vanadium alloying element were detected in the oxide. This is confirmed by the evolution of Al/Ti and V/Ti ratios with depth (Figure 4). Iron is present in the oxide scale grown on all three alloys. The maximum of Fe/Ti ratio depends on the alloy: it is about 15, 5 and 17 % for T40, TA6V and Ti10-2-3, respectively. Figure 5 shows the three alloys sample surfaces and cross sections after 434 h exposure to primary water. For all alloys, observations indicate that the oxide scale is constituted of a dense continuous oxide layer as well as small and large surface oxide crystallites. However, morphologies of the dense continuous oxide layer and small oxide crystallites differ according to the alloy. T40 oxide appears to be constituted of a dense continuous 30 nm-thick oxide layer topped by well-separated small oxide crystallites. TA6V and Ti10-2-3 scales are constituted of a dense continuous oxide layer with joined small oxide crystallites which at this scale were not differentiable from the dense continuous oxide layer. For TA6V and Ti10-2-3, in areas where such small crystallites are absent, the dense oxide layer thickness was estimated around 30 nm. Where small crystallites are present, there is no apparent separation between the dense continuous oxide layer and these small crystallites; the thickness of this overall oxide layer reaching 300 nm locally. Small crystallites' diameter was found of about 1 µm for T40 and lower than 1 µm for both TA6V and Ti10-2-3.

There is also a variation of large crystallites size and distribution depending on the alloy after 434 h. Images analysis indicate that large crystallites diameter averages 3 µm for T40 and Ti10-2-3 and around 10 µm for TA6V. Surface large crystallites densities were estimated around 5000 mm -2 , 1000 mm -2 and 10000 mm -2 for T40, TA6V and Ti10-2-3, respectively. Evolution of crystallites size with exposure duration is weak. Nevertheless, the tendency which seems to emerge from surface observations for all alloys is the increase of large crystallites size with exposure duration while small crystallites size stays almost steady. T40 samples cross sections after 217 h, 434 h and 834 h exposure to primary water (Figure 6) reveal that oxide-metal interface tortuosity increases with exposure duration. Corrosion penetration depth reaches 100 nm, 200 nm and 400 nm for 217 h, 434 h and 834 h exposure duration, respectively. The evolutions of TA6V and Ti10-2-3 oxide-metal interface morphologies are similar to the one on T40. For TA6V and Ti10-2-3, penetrations do not seem to be located preferentially in  or  phase. Figure 6 also shows that the dense continuous oxide layer thickness of T40 stays around 30 nm during exposure from 217 h to 834 h. The dense continuous oxide layer thickness of TA6V and Ti10-2-3 is also around 30 nm and constant during exposure from 217 h to 834 h.

Figure 6: SEM observation (secondary electrons) of T40 samples cross sections after 217 h, 434 h and 834 h exposure to primary water

T40 sample cross section after gold markers deposit and 89 h exposure is presented in Figure 7. Gold markers are situated just above the dense oxide layer, demonstrating its inward growth. However, markers are below small crystallites (as shown in Figure 7) and large oxide crystallites. The morphologies of TA6V and Ti10-2-3 cross sections after 89 h exposure with preliminary gold deposit are similar to the one observed on T40. Figure 11 illustrates XPS surface spectra of Al-2p core level of both non-exposed and 24 h exposed TA6V samples. XPS characterisation depth is around 10 nm. The analysed surface was around 0.25 mm 2 . Detection limit is between 0.1 and 1 at. % according to the element. Concerning the non-exposed sample spectra (in red), the peak located at 74.4 eV indicates that the native oxide layer contains aluminium cations (green contribution) [START_REF] Jeurgens | Determination of thickness and composition of aluminium-oxide overlayers on aluminium substrates[END_REF]. However, the absence of peak at the same binding energy for the 24 h exposed sample spectrum means that no aluminium cation was detected at the oxide scale surface after 24 h exposure. Results obtained for both non exposed and 24 h exposed Ti10-2-3 samples were similar to TA6V ones.

XPS spectra of Al-2p core levels versus etching durations of a TA6V sample exposed 24 h in primary water is plotted in Figure 12. The absence of intensity at the binding energy of 74.4 eV for all etching durations indicate that no aluminium cation is detected in the whole oxide scale after this exposure time.

The result obtained for 24 h exposed Ti10-2-3 samples is similar for TA6V.

Vanadium was not detected by XPS analysis in the TA6V native oxide scale nor in the TA6V oxide scale formed after 24 h exposure to media. However, vanadium was detected by XPS in Ti10-2-3 native oxide scale whereas it was not in the Ti10-2-3 oxide scale formed after 24 h exposure to primary water. Classical linear transform described elsewhere [START_REF] Gärtner | Depletion-Layer Photoeffects in Semiconductors[END_REF], [START_REF] Butler | Photoelectrolysis and physical properties of the semiconducting electrode WO2[END_REF] ((I ph .E) 0.5 vs. E) was plotted for all alloys to evaluate oxide bandgap energies (E g ). Table 2 presents the bandgap energy values measured for T40, TA6V and Ti10-2-3 oxides layers obtained after 24 h, 434 h and 3499 h exposure. Bandgap energies obtained for TA6V and Ti10-2-3 are centered on 3.2 eV for all investigated exposure durations. The bandgap energy obtained for T40 was smaller than those on TA6V and Ti10-2-3. It decreases from 3.08 eV to 3.00 eV for exposure durations going from 24 h to 3499 h.

Similarities and differences between the three alloys oxide scales

Table 3 shows a summary of the similarities and differences observed after exposure of T40, TA6V and Ti10-2-3 samples to primary water. Several results indicate that some characteristics of the oxide scales formed do not depend on the alloy. Indeed, a dense continuous oxide layer, small oxide crystallites and large crystallites were observed by SEM on the three alloys. Inward growth of the dense continuous oxide layer of all alloys was also evidenced thanks to gold markers. XRD analysis showed that the oxide scale of all alloys contain TiO 2 anatase and FeTiO 3 ilmenite. Moreover, Raman, EDS and ASTAR analysis enabled to assign these oxide phases to small and large crystallites, respectively. Finally, PEC characterisations showed that oxides of the three alloys are all n-type semiconductors. Concerning the evolution of oxides morphologies and nature with exposure duration, results point out that dense continuous oxide layer thickness and small oxide crystallite diameter remain constant and that large oxide crystallite diameter increases, for all alloys. Oxide-metal interface tortuosity also increases with exposure duration for all alloys. Raman analysis do not reveal any evolution of small and large oxide crystallites nature with exposure duration.

Other results indicate some differences between the oxide characteristics of the studied alloys. XRD analysis revealed the presence of TiO 2 rutile in T40 oxide scale only. ASTAR phase map showed that the dense oxide layer of T40 was made of TiO 2 rutile and that the dense oxide layer of the two other alloys was made of TiO 2 anatase. Moreover, SEM cross section observations point out oxide morphology differences. Indeed, the T40 dense continuous oxide layer is topped by small and large oxide crystallites whereas small crystallites of TA6V and Ti10-2-3 are part of the dense continuous oxide layer. Finally, a significant difference between the bandgap of the T40 oxide layer and the bandgap of both TA6V and Ti10-2-3 oxide layers was measured by PEC.

DISCUSSION

Dissolution and precipitation phenomenon

The increase of interface tortuosity with time (Figure 6) along with the steadiness of the dense continuous oxide layer thickness indicate that this TiO 2 layer undergoes a dissolution phenomenon during exposure to the water at the same time as it grows inwards. It is consistent with the significant value of the TiO 2 solubility in water in the conditions of the study (around 1.2 10 -8 mol.kg -1 water [START_REF] Knauss | Ti (IV) hydrolysis constants derived from rutile solubility measurements made from 100 to 300 C[END_REF]). Titanium species potentially formed by dissolution of TiO 2 in water at pH 7.5 and 300 °C are Ti(OH) [START_REF] Kofstad | Investigation on the oxidation mechanism of titanium[END_REF] and Ti(OH) 5 -hydroxides [START_REF] Cook | Pourbaix diagrams for chromium, aluminum and titanium extended to high-subcritical and low-supercritical conditions[END_REF].

The presence of TiO 2 anatase crystallites at the dense oxide layer surface of all alloys for all exposure durations indicates that dissolved titanium hydroxides have then precipitated. These species locally reached their solubility limit in spite of the use of ion-exchange resin. It could result from the low water flow (5 L.h -1 ) and the resulting low water speed inside reactor (2 mm.s -1 ). Water flow was maybe not high enough to prevent enrichment in hydroxides inside reactor, especially in the vicinity of the sample surfaces. Solubility of a species may vary with allotropic form and size of the crystal where it comes from.

Indeed, small crystals are generally more soluble than big ones because of the increase of the specific surface energy with decreasing crystal size [START_REF] Kahlweit | Ostwald ripening of precipitates[END_REF]. It could explain why TiO 2 dense layer may dissolve and why hydroxides may precipitate into TiO 2 crystallites at the same time.

FeTiO 3 ilmenite crystallites certainly originate from the co-precipitation of titanium and iron hydroxides dissolved in medium. It is unlikely that iron hydroxides come from samples oxide dissolution. Indeed, ilmenite crystallites were found in an undifferentiated way on all alloy samples even though iron is not an alloying element of T40. Moreover, the main source of iron is stainless steel loop oxide dissolution.

Presence of aluminium in the TiO 2 oxide layer of TA6V and Ti10-2-3?

GD-OES concentration profiles for TA6V and Ti10-2-3 (Figure 3 and Figure 4) do not detect aluminium in the global oxide scale. Yet, it is not possible to conclude whether the dense continuous oxide layer close to the metal is enriched in alloying elements or not by GD-OES analysis alone. Indeed, the roughness of the oxide scales leads to a blurry concentration profile of all elements. Aluminium cations were detected by XPS analysis in the native oxide scale (before exposition) (Figure 11) whereas they were not in the TA6V oxide scale after exposure to medium (Figure 12).

The 25 cc TPN H 2 .kg -1 water set experimentally led to a ratio of water molecule on hydrogen molecule activities (a(H 2 O)/a(H 2 )) around 1000. This value which is indicative of the redox characteristic of the water is much higher than the ratio of the frontier stability domain of Al and Al 2 O 3 at 300°C (a(H 2 O)/a(H 2 ) = 1.10 -27 ) [26], [START_REF] Kubaschewski | Metallurgical Thermochemistry[END_REF]. It means that aluminium oxide is widely thermodynamically stable in the experimental conditions. Therefore it is believed that aluminium cations are formed and are present in the inwards growing TiO 2 oxide scale although they are not detected by XPS analysis. Indeed, the volume fraction of dense continuous oxide layer is low in comparison to the overall oxide volume and the aluminium minimum detectable fraction by XPS analysis is relatively high (due to the low sensibility factor of aluminium). The absence of aluminium enrichment in the metal just below the oxide scale (Figure 4) support this hypothesis.

Studies of high temperature oxidation of titanium alloys in gaseous environment indicate various distribution of alumina inside titanium oxide scales. On one hand, alumina grains were found to be finely scattered through TiO 2 scale [1], [START_REF] Motte | A comparative study of the oxidation with water vapor of pure titanium and of Ti-6Al-4V[END_REF], [START_REF] Sarrazin | Mechanism of oxidation of titanium and alloy ti-6al-4v by water-vapor[END_REF]. On the other hand, an alumina layer was observed at the external TiO 2 interface. Kaneda et al. who studied corrosion of titanium alloys in supercritical water at 550 °C also observed an alumina layer at the external TiO 2 interface [START_REF] Kaneda | General corrosion properties of titanium based alloys for the fuel claddings in the supercritical water-cooled reactor[END_REF]. Thus, alumina was never observed at the interface between TiO 2 and the metal. Even though temperature involved in these studies are higher than 300 °C, it could be suggested that, in the present study, aluminium cations diffusion from the metal/TiO 2 interface towards the TiO 2 /water interface occurred during exposure of TA6V and Ti10-2-3 samples at 300 °C to primary water.

Finally, the high solubility of aluminium oxides in water at 300 °C and pH 7.5 (around 10 -3 mol.kg -1 water [START_REF] Bénézeth | Aqueous high-temperature solubility studies. II. The solubility of boehmite at 0.03 m ionic strength as a function of temperature and pH as determined by in situ measurements[END_REF]) may explain the dissolution of aluminium cations as soon as they reach the oxide/water interface. Thus, the external side of the oxide scale would never be enriched in aluminium cations, which is consistent with the fact they were not detected by XPS analysis.

For TA6V, vanadium was not detected in the native oxide scale nor in the oxide scale formed after 24 h exposure to media. However, vanadium was detected by XPS in Ti10-2-3 native oxide scale whereas it was not in the Ti10-2-3 oxide scale formed after 24 h exposure to primary water. As aluminium oxide Al 2 O 3 , vanadium oxides like V 2 O 5 or VO 2 are thermodynamically stable in the experimental conditions. As for aluminium, there was no vanadium enrichment in the metal just below the oxide scale (Figure 4). Motte et al. as well as Kaneda et al. who studied corrosion of TA6V in air between 850 °C and 950 °C and in supercritical water at 500 °C, respectively, founded vanadium dispersed in the whole TiO 2 oxide scale. Thus, it could be suggested that vanadium cations, as aluminium cations, were formed and were present in the dense TiO 2 oxide scale of TA6V and Ti10-2-3 and were dissolved inside media as soon their reach oxide/water interface.

Causes and consequences of the rutile or anatase formation

XRD analysis revealed the presence of rutile on T40 oxide only. ASTAR phase maps show that the dense oxide layer grown on T40 was made of rutile. These results are consistent with bandgap measured by PEC for T40 oxide (E g ≈ 3.0 eV). Indeed, this value is close to bandgap value of rutile found in literature (E g (rutile) ≈ 2.95 eV) [START_REF] Wouters | Oxydation thermique des metaux dans la vapeur d'eau. Cas du nickel et du titane[END_REF]. In the same way, the presence of the dense TiO 2 anatase layer on TA6V and Ti10-2-3 oxides revealed on ASTAR phase maps is consistent with the bandgap value (E g ≈ 3.2 eV) which is in good agreement with values found for anatase in literature (E g (anatase) ≈ 3.2 eV) [START_REF] Wouters | Oxydation thermique des metaux dans la vapeur d'eau. Cas du nickel et du titane[END_REF].

The fact that the dense TiO 2 rutile layer of T40 was not detected by Raman analysis means its concentration is lower than the detection limit of Raman (≈ 1 %) in the volume analysed. The volumic ratio of T40 dense continuous oxide layer on the overall oxide is very low according to its thickness (≈ 30 nm).

Rutile is for all temperature and pressure conditions the thermodynamic stable phase of TiO 2 . However, low temperature prevents the anatase to rutile transformation (ART) due to kinetics. ART temperature varies from 400 °C to 1000 °C depending both on oxidation environment and impurities in the oxide [START_REF] Gennari | Kinetics of the anatase-rutile transformation in TiO2 in the presence of Fe2O3[END_REF].

Hydrogen atmosphere as well as alkaline and transition metal ions impurities in the oxide scale as Li 2 O was found to increase the ART kinetics [START_REF] Sarrazin | Mechanism of oxidation of titanium and alloy ti-6al-4v by water-vapor[END_REF], [START_REF] Gennari | Kinetics of the anatase-rutile transformation in TiO2 in the presence of Fe2O3[END_REF], [START_REF] Shannon | Phase Transformation Studies in TiO2 Supporting Different Defect Mechanisms in Vacuum-Reduced and Hydrogen-Reduced Rutile[END_REF]. It was suggested it could be due to the increase of oxygen vacancies number by hydrogen atmosphere and alkaline and transition metal doping. Vanadium doping was also found to increase ART kinetics [START_REF] Khatun | Anatase to rutile phase transition promoted by vanadium substitution in TiO 2 : A structural, vibrational and optoelectronic study[END_REF]. On the contrary, oxidation in O 2 was found to inhibit the transformations by filling oxide vacancies [START_REF] Sarrazin | Mechanism of oxidation of titanium and alloy ti-6al-4v by water-vapor[END_REF]. In the same way, Vásquez et al. have pointed out that aluminium doping do stabilise TiO 2 anatase phase with respect to rutile phase [START_REF] Vásquez | Influence of Fe and Al doping on the stabilization of the anatase phase in TiO 2 nanoparticles[END_REF].

The water used in the present study contained lithium and hydrogen. It is then suggested that lithium and/or hydrogen were incorporated (in very low concentration) in the dense oxide layer of all alloys samples leading to a decrease of the ART temperature. The ART temperature may have reached values lower than 300 °C explaining the rutile presence in dense oxide layer of T40. It is also suggested that concentration of some alloying element cations as aluminium which diffused inside TA6V and Ti10-2-3 dense oxide layer may have been high enough to increase ART temperature and then stabilise anatase regarding rutile. The absence of aluminium in T40 chemical composition may then explain the rutile dense oxide layer of T40.

The difference in the allotropic form of the dense continuous layer formed on T40 on one hand and on TA6V and Ti10-2-3 on the other hand could be the cause of the difference in oxide morphologies between T40 and the two other alloys. Indeed, TA6V and Ti10-2-3 small oxides crystallites may be part of the dense oxide layer because of their identical allotropic form (anatase), favouring epitaxial growth. On the contrary, T40 small oxide crystallites may be on the top of the dense oxide layer because of their different allotropic form (anatase and rutile, respectively). Thus, hydroxide precipitation phenomenon may have involved small crystallites nucleation and growth for T40 and small crystallites growth only for TA6V and Ti10-2-3.

Oxygen penetration in the metal?

GD-OES oxygen concentration profiles do not exhibit any oxygen enrichment in the metal bulk below the oxide scale for the three alloys after 434 h exposure (Figure 3). Nanoindentation results obtained on T40 for the same exposure time (Figure 14) are also consistent with the absence of significant oxygen penetration in the metal beyond 1 µm. Indeed, oxygen diffusion in the metal would have enhanced the hardness proportionally to its concentration [START_REF]Highlights in mineralogical crystallography[END_REF].

This result is consistent with the low diffusion coefficient of oxygen in  titanium at 300 °C. It varies from 2.6 10 -19 cm².s -1 to 8.8 10 -18 cm².s -1 according to literature [START_REF] David | Measurement of diffusion-coefficient of oxygen in alpha titanium between 460-degrees and 750-degrees-c by use of the nuclear-reaction o-16(d,p)o-17x[END_REF], [START_REF] Rogers | Low temperature diffusion of oxygen in titanium and titanium oxide films[END_REF]. Theoretical diffusion depth calculated accordingly (2(D.t) 1/2 ) varies from 10 to 70 nm for a 434 h duration exposure. Such oxygen penetrations are undetectable by nanoindentation and GD-OES analysis because of the resolution limit of these experimental techniques. Several studies described titanium oxide dissolution linked to the oxygen diffusion in the metal bulk at temperature higher than 380 °C -400 °C [START_REF] Mizuno | Temperature dependence of oxide decomposition on titanium surfaces in ultrahigh vacuum[END_REF]- [START_REF] Lu | Oxidation of a polycrystalline titanium surface by oxygen and water[END_REF]. It means that degradation of titanium linked to an oxygen enriched area formation does not seem to be an issue at 400 °C.

To the authors' knowledge, no data is available on the diffusion coefficient of oxygen in  titanium phase at 300 °C. Nevertheless, diffusion of oxygen in  phase at higher temperature (800 °C) is around 100 times faster than in  phase [START_REF] Rosa | Oxygen diffusion in alpha and beta titanium in the temperature range of 932° to 1142°C[END_REF], [START_REF] Song | Diffusion of oxygen in β-titanium[END_REF], [START_REF] Elliott | Diffusion in Titanium and Titanium Alloys[END_REF]. However, no -case formation was observed by SEM on TA6V and Ti10-2-3 samples cross sections for all exposure durations. Thus, it could be suggested that oxygen diffusion in  phase at 300 °C is also low enough to prevent any oxygen enriched area formation.

Point defect diffusing in dense oxide layer

T40 sample exposure to primary water with prior gold marker deposit indicates the inward growth of the TiO 2 dense oxide layer (Figure 7). However, gold markers appear to be located below small oxide crystallites. It is suggested that the inner part of the oxide grew by oxidation process and that the outer part of the oxide (oxide crystallites) was formed by the precipitation of titanium hydroxides. The oxidation mechanisms of TA6V and Ti10-2-3 also involved an inward growth of the oxide layer. Finally, oxide layer grown by oxidation process of the three alloys involved an anionic growth.

The anionic growth of the dense oxide layer could be attributed to both interstitial oxygen and oxygen vacancies point defects diffusion. PEC characterisations performed on samples indicate the increase of the photocurrent (I ph ) with applied potential (Figure 13 c)). Such an evolution is the evidence of the n-type semiconductivity of the characterised oxide. This result is consistent with TiO 2 conduction types determined in several studies [41] [42]. Thus, it is possible to conclude that the main point defects responsible for the growth of TiO 2 oxide layer are oxygen vacancies. Nevertheless, more characterisations would be necessary to determine the species diffusing inside oxygen vacancies from the external interface toward the internal interface of the oxide layer. In literature, these species are suggested to be oxygen anions or hydroxide anions in dry and water vapour [START_REF] Wouters | Thermal oxidation of titanium by water vapour[END_REF] environment, respectively.

CONCLUSION

Polished samples of T40, TA6V and Ti10-2-3 titanium alloys have been exposed to primary water (T = 300 °C, P = 150 bar, [H 2 ] = 25 cc TPN.kg -1 of water, [Li] = 0.9 wt. ppm, theoretical pH = 7.5) for durations ranging from 24 h to 3499 h.  Oxide scale developed on all three alloys is made of a continuous 30 nm thick layer topped by small and large cirstallites. T40 oxide scale is made of a dense continuous TiO 2 rutile layer topped by small TiO 2 anatase and large FeTiO 3 ilmenite crystallites. The TA6V and Ti10-2-3 small TiO 2 anatase crystallites are part of the continuous TiO 2 anatase layer. TA6V and Ti10-2-3 dense oxide layer are also topped by large ilmenite crystallites. After 434 h exposure, ilmenite crystallites diameter reach 10 µm, anatase crystallites diameter is around 1 µm for T40 and lower than 1 µm for TA6V and Ti10-2-3.  Presence of crystallites and the increase of the interface tortuosity with exposure time as well as the constant dense oxide layer thickness with exposure time demonstrates the extent of oxide dissolution and hydroxide precipitation phenomenon.  Corrosion penetration depth reaches around 400 nm for the three alloys after 834 h exposure.

For TA6V and Ti10-2-3, penetrations do not seem to be located preferentially in  or  phase.  Oxygen diffusion inside the metal below the oxide layer was limited owing to the low temperature involved.  Inward growth and n-type semiconductivity of all alloys oxides have been observed and measured. Thus, oxygen vacancies were the main point defect diffusing inside the dense continuous TiO 2 layer.

It is suggested that aluminium cations were formed and diffused from the metal/TiO 2 interface toward the TiO 2 /media interface during exposure of TA6V and Ti10-2-3 samples. Aluminium cations may have dissolved as soon as they reached the oxide/media interface because of their high solubility in water in such conditions. It is suggested that rutile may have been formed on T40 because of the effect of hydrogen or lithium oxide doping coming from the water. It is also suggested that only TiO 2 anatase was formed on TA6V and Ti10-2-3 because of aluminium doping coming from alloys. Further investigations will be focused on dissolution and corrosion kinetics.

Figure 1 :

 1 Figure 1: Microstructure of the studied T40 observed by optical microscopy with polarised light and microstructures of TA6V and Ti10-2-3 observed by SEM in backscattered electrons mode.

Figure 2 :

 2 Figure 2: Diffractograms of T40, TA6V and Ti10-2-3 samples exposed 434 h

Figure 3 : 3 Figure 4 :

 334 Figure 3: GD-OES concentrations profiles of samples after 434 h exposure to primary water; a) T40 b) TA6V and c) Ti10-2-3

Figure 5 :

 5 Figure 5: SEM observation (secondary electrons) of T40, TA6V and Ti10-2-3 samples after 434 h exposure to primary water; a) surface and b) cross sections

Figure 7 :

 7 Figure 7: SEM observation (secondary electrons) of T40 sample cross sections after 89 h exposure to primary water with preliminary gold marker deposit

Figure 8 :

 8 Figure 8: EDS mapping (E = 20 kV) of T40 sample surface exposed 434 h

Figure 9 :

 9 Figure 9: Raman analysis of T40 sample surface exposed 834 h to primary water (dash lines) ; a) small crystallites and dense oxide layer and b) large crystallites ; solid lines refer to Raman spectra from reference [19] ; a) TiO 2 anatase and b) FeTiO 3 ilmenite.

Figure 10

 10 Figure10presents ASTAR phase combined to correlation index maps of oxides scale of the three alloys after 434 h. The probe size and the step size measure 1 nm and 3 nm, respectively. The phase map of TA6V was located in a metallic area without  phase. The phase map of the three alloys were performed on areas free from large crystallites. Phase maps show that small crystallites of the three alloys are made of TiO 2 anatase. The dense oxide layer grown on T40 is composed of TiO 2 rutile (yellow layer) whereas for TA6V and Ti10-2-3 it consists in TiO 2 anatase. Grain diameters of both anatase and rutile dense TiO 2 scale is in the order of few 10 nm.

Figure 10 :

 10 Figure 10: ASTAR phase combined to correlation index maps of oxide scales on T40, TA6V and Ti10-2-3 samples exposed 434 h to primary water

Figure 11 :Figure 12 :

 1112 Figure 11 : Al 2p XPS surface spectra of non-exposed and 24 h exposed TA6V samples

Figure 13 :

 13 Figure 13: a) Photocurrent (I ph ) and dephasing angle () versus incident light energy (E) for different potential varying from -420 mV/MSE to 280 mV/MSE of T40 sample after 3499 h exposure. c) Photocurrent versus potential at 3.76 eV.

Figure 14

 14 Figure14shows the hardness profile of a T40 sample cross section exposed 434 h to primary water obtained by nanoindentation. The profile plotted in the figure is the average of values coming from five different profiles. It indicates that hardness of metal below the oxide layer is constant with depth. The large uncertainty of the first hardness value just below the oxide is due to the fact that some indents encroach upon the oxide and the nickel deposit on top of the oxide. Hardness was not measured on TA6V and Ti10-2-3 samples cross sections because of the overly difference between  and  phase hardness.

Figure 14 :

 14 Figure 14: Nanoindentation hardness profile of T40 sample cross section after 434 h exposure

Table 1 : Billets chemical composition (Ti = bal.)

 1

	wt. %	Al	V	Fe	C	O
	T40	0.01 0.01 0.03 0.003 0.16
	TA6V	6.38 4.12 0.16 0.008 0.17
	Ti10-2-3 2.96 9.73 2.15 0.011 0.09

Table 2 : Oxides bandgap energies (E g ) after 24 h, 434 h and 3499 h exposure to primary water

 2

	E g (eV) T40 TA6V Ti10-2-3
	24 h	3.08 3.23	3.20
	434 h	3.02 3.24	3.25
	3499 h 3.00 3.18	3.19

Table 3 : Summary of results obtained for the three alloys

 3

	Techniques	Results	T40	TA6V	Ti10-2-3
		TiO 2 anatase	Yes	Yes	Yes
	XRD	TiO 2 rutile	Yes	No	No
		Ilmenite	Yes	Yes	Yes
		Inward growth of dense oxide layer	Yes	Yes	Yes
	SEM	Small crystallites
		are part of dense	No	Yes	Yes
		oxide layer
	Raman	Small crystallites are TiO 2 anatase Large crystallites are FeTiO 3 ilmenite	Yes Yes	Yes Yes	Yes Yes
	ASTAR	Nature of dense oxide layer	TiO 2 rutile	TiO 2 anatase	TiO 2 anatase
	PEC	n-type semiconduction	Yes	Yes	Yes
		E g	≈ 3.0 eV	≈ 3.2 eV	≈ 3.2 eV

ACKNOWLEDGEMENTS

The authors wish to thank Etienne Amblard, Claire Berthier, Sylvie Poissonnet and Frédéric Datcharry from CEA Saclay for their precious contributions on experimental and characterisations ressources.