## 2 Literature Review
The same authors [49; 50; 51] studied the influence of the number of neurons in an ANN to efficiently detect defects in composite material using PT. In their study [PERSON] et al. used both synthetic data and acquired data. [PERSON] et al. [49] concluded that the size of the dataset and the number of neurons have a apparent influence on the performance of the algorithms. [PERSON] et al. [52] reviewed the performance of PCA and TSR methods used preprocessing before training an ANN and The defect detection ability and the depth prediction are investigated. They proved that ANN offers a great performance on both tasks. [PERSON] et al. [53] compared the performances of PPT [8], TSR [37], Apparent Thermal Inertia (ATI) [54], Thermal Quadrupoles (TQ) [55], Non-linear fitting (NLF) [56], and ANN [57] to accurately predict the depth of defects. They used acquired and simulated PT data and found that ANN outperforms other methods. They reach a similar conclusion as [PERSON] et al. [49], that size of the training set has a great impact on the quality of the results. [PERSON] et al. [58] investigated the ability of ANN to successfully find defects in a CFRP and identifying the foreign material that fills them. They proved that ANN performs very well for such applications. [PERSON] et al. [59] used a cross-learning strategy based in order to train an ANN to identify defects. This strategy consists in training the feature stage of the algorithm on both each pixel of an image and its representation in the sequence overlay matrix. This approach allows for each pixel to extract spatial features from the images and also temporal features from the overlay matrix. Once the features are extracted, a second ANN is trained using a state-of-the-art method to provide a mask of the defect regions. Several deep learning architectures for segmentation are investigated in this study which shows fair results. [PERSON] et al. [60] employed a Generative Adversarial Networks (GAN) for defect detection and segmentation and compared it with several state-of-the-art deep learning frameworks. This study shows that GANs outperformed the other state-of-the-art approaches. Later the same authors introduced DeftectNet [61], a GAN-based architecture, made for the defect detection in CFRP sample evaluated by PT. This study shows that GANs outperformed the other state-of-the-art approaches. [PERSON] et al. [62] used two state-of-the-art object recognition algorithms [63; 64] in an attempt to localize defects. Note that the algorithm [63] is also able to provide a mask of the shape of the detected object. [PERSON] et al. did not obtain good results in their experiments. [PERSON] et al. [65] tried a similar experiment as the one made by [PERSON] et al. [43], using another algorithm. The work of [PERSON] et al., was based on a state-of-the-art object detection algorithm [66] in order to localize the defects. [PERSON] et al., used a training set based on images recorded from PT experiments of several materials. They reported poor results. [PERSON] et al. [67] had a similar idea, but used both another architecture [68]. The training sets were composed of images collected from the Internet, while the testing datasets were PT experiments of two CFRP samples. The results of [PERSON] et al. were similar to those of [PERSON] et al.Later [PERSON] et al. [69] evaluated the same model proposed by [PERSON] et al. [63] on both synthetic and experimental PT data. [PERSON] et al., showed that using synthetic data can improve the performance in terms of detection. [PERSON] et al. [70] proposed a UNet [71] inspired network to segment defects in curved CFRP samples using PT data acquired by both Long Wave InfraRed (LWIR) and Mid Wave InfraRed (MVIR) cameras. One can note that the great majority of the works regarding defect detection in PT commonly use phase analysis, linear algebra or machine learning approaches. However other approaches have been proposed. [PERSON] et al.