## 1 Introduction  
Material manufacturing processes are not defect free and nondestructive testing techniques (NDT) are required to ensure quality standards. Driven by low costs and inspection times, ultrasonic testing (UT) is one of the most widely used types of NDT. It consists of the transmission of ultrasonic waves through the component. Any defect that is present produces a reflected wave that can be measured at the electronic transducer. The signal produced by the propagation of the ultrasonic wave is called A-scan, and its analysis can provide relevant information about the defect, the component or the material. B-scan refers to the image produced when the data collected from an ultrasonic inspection is plotted on a cross-sectional view of the component. The cross-section will normally be the plane through which the individual A-scans have been collected. C-scan refers to the image produced when the data collected from an ultrasonic inspection is plotted on a plan view of the component. A C-scan does not have to be a single cross-section. Indeed, it often shows a combination of measurements obtained across the whole thickness. B-scan and C-scan representations are often used because ultrasonic signals are hard to interpret. Defect detection and measurement of defects from ultrasonic data is a manual task. Therefore, it calls for skilled technicians and is a time-consuming process subject to human error. The successful application of machine learning, and more specifically deep learning in the field of computer vision, within different neural networks architectures [1; 2; 3; 4; 5] and object detection [6; 7; 8] has increased the interest in models to automatically detect defects in ultrasonic data. [PERSON] et al. [9] developed support vector machines to identify defects. [PERSON] and [PERSON] [10] created a dataset of ultrasonic images, benchmarked several object detection deep learning architectures, and publicly shared the data and codes. [PERSON] et al. [11] used a convolutional neural network (CNN) to identify and size up defects in phased array data. [PERSON] et al. [12] proved that EfficientDET [13] was better at detecting defects than RetinaNet [14], and YOLOv3 [7]. They suggested a novel procedure for calculating anchors. Furthermore, they developed a large dataset of phased array ultrasonic images, which they were able to use to conduct a robust validation of their results using a 5-fold cross-validation. In a more recent work [15], [PERSON] et al. developed two approaches to incorporate the A-scan signal surrounding a defect area into the detection architecture. One approach was based on model expansion, the second method extracts the features separately, and they are then merged in the detection head. [PERSON] et al. [16] developed a data augmentation technique adapted to the ultrasonic NDT domain to train another CNN, based on the architecture of VGG [2]. The above works have several points in common: inspections are carried out on metal materials, defects (side-drilled holes, flat bottom holes, thermal fatigue cracks, mechanical fatigue cracks, electric discharge machined notches...) are artificially introduced, and detection/segmentation are performed in the B-scan mode. Today, composite materials, in particular carbon fiber reinforced polymers (CFRP), are increasingly used in the aerospace, automotive, and construction industries in order to create lightweight structures and achieve more efficient transportation mechanisms. CFRP are inhomogeneous materials. This poses an obstacle to the application of automatic defect detection models due to noise artefacts [17]. In spite of this, automatic defect detection tasks are possible. [PERSON] et al. [18] developed a convolutional neural network together with wavelet transformations to classify A-scan signals that correspond to defects inserted at different depths during manufacturing. In addition, they developed a post-processing scheme to interpret the classifier outputs and visualize the locations of defects using a 3D model. [PERSON] et al.