# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
### Related Works
From this point, a single neural network predicts class probabilities and bounding boxes from full images. RetinaNet is a one-stage method; however, it is able to achieve the precision of two-stage methods without losing the speed of its class methods because it addresses the class imbalance between foreground and background [20]. Methods based on a two-stage approach are more common and widespread within the world of DL object detection. Thus, R-CNN [23] is made up of three parts. In the first, proposals for independent candidate regions of each category are generated. The second part is made up of one that extracts features of fixed length. The third part is a set of SVMs. The second module is a large convolutional neural network that extracts a feature of a fixed-length vector of each region. The third module is a set of class-specific linear Support Vector Machines (SVMs). R-FCN, where fully convolutional region-based networks are presented for accurate and efficient object detection. Unlike other models like Fast/Faster R-CNN, which apply a costly subnet per region hundreds of times, in this case the calculations are shared across the entire image, proposing scoremaps relative to position [24]. In the case of Fast R-CNN, this method is based on efficiently classifying object proposals using deep convolutional networks. Compared to previous works, Fast R-CNN brings innovations to improve training and test speed while increasing object detection accuracy [25]. As delineated in the extant literature [26], the Faster R-CNN model is presented with an RPN that exhibits convolutional characteristics analogous to those of the detection network.We already know that an RPN is a fully convolutional network capable of predicting both object limits and object scores at each position. In this case, the RPN generates high-quality region proposals, which Fast R-CNN uses for detection, merging RPN and Fast R-CNN into a single network that shares features, making a substantial improvement on the latter. Alternatively, Mask-RCNN [27] is a refinement of Faster R-CNN, incorporating an additional branch to predict an object mask in parallel with the existing branch that recognizes bounding boxes. A review of the extant bibliography yielded several articles addressing the creation and provision of image datasets focused on the detection of weld defects. Thus, in the first place, we mention the GDXray database, free to use, that the authors present in [12]. It is a database of images taken with X-rays, intended to perform non-destructive tests, not being explicitly of welding defects. The authors of [24], while not releasing the image repository to the public, propose the establishment of an image database, in which the frequency is even mentioned, for the detection of defective spot welds. They focus on studying performance in small datasets and with unbalanced classes, the images being taken with a high-resolution camera. In the same way, taking the images with a high-resolution color camera, in the work presented in [10], the authors propose a welding surface inspection of armatures. They use a CNN-based approach. comparing various CNN models against each other. In both cases, the authors do not make the image dataset public so that it can be studied in other research. The investigation carried out in [28] shows radiographic imaging to detect welding defects in shipbuilding. Although the authors show us the number of images taken, the percentage composition of the train, validation, and test groups. a reference to the dataset under consideration is noticeably absent. Definitely, some previous research has been found for automated recognition of surface weld defects based on DL. In few works, new datasets are used, or the data used for training are not offered publicly. The GDXray database is the most frequently used and accessible of the datasets, as referenced in the previous paragraph.