# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
### Related Works  
In recent years, a range of methods and models based on the concept of neural networks, in their various variants, have been proposed by other researchers to address the tasks of inspection, monitoring, or diagnosis of a weld. These methods have been employed in various welding processes across multiple sectors. A study presented in Liu [8] employed an artificial neural network (ANN) for the purpose of predicting welding residual stress and deformation in the context of electro-gas welding. Thus, the authors propose in [9] a real-time monitoring system for laser welding of tailor rolled blanks using CNN. As is the case of [1], where the authors propose the detection of weld defects using a type of CNN called Faster R-CNN, trained with X-ray images of the weld seams. As indicated in reference [10], a welding surface Inspection of Armatures system is demonstrated through the use of CNN and image comparison. In a similar manner, the authors of [11] utilize a deep learning (DL) technique to detect defects in the gas metal arc welding (GMAW) process. The authors conducted ultrasonic, magnetoscopic, and penetrant liquid tests on the welds, though they do not share the dataset used. The reviewed studies sometimes use their own images to train their artificial neural networks. On other occasions, the works themselves, such as the one presented, have created a dataset of images for the occasion, as is the case of [12], where a database of X-ray images corresponding to five categories is incorporated: castings, welds, baggage, natural objects, and settings. As delineated in [13], the system was developed for the specific purpose of facilitating welding training. It involves the use of a camera-based system that captures a series of images of welding seams. These images are then processed using a CNN to train the system. In the case of [14; 15], the authors propose an unsupervised learning model using their own radiographic images and other calculations. In the same way, the authors in [16] create their own dataset of radiographic images to train a DL system based on VGGnet [17]. Traditionally, neural network architectures for object detection are often divided into two categories called one-stage or two-stage. In two-stage detector methods, a Region Proposal Network (RPN) is created. RPN is used for generating high-quality object proposals, which are very vital to improving the performance of detectors [18; 19]. This RPN is used to  
Figure 2: Scheme of the GMAW welding process. generate the Regions Of Interest (ROI), proposed in the first stage. These ROI proposals are subsequently used for classification and bounding box regression in the second stage. Two-stage methods have the disadvantage that they are usually slower; however, they have higher precision than one-stage methods. On the other hand, one-stage detectors do not preselect to generate candidates like two-stage methods. They directly attempt to detect the objects by solving a simple regression problem. That is why one-step methods are faster in their resolution but produce results with less precision [20]. In the same way, Single Shot Detection (SSD) is presented by its authors [21] as a method to detect objects in images using a single deep neural network in a single stage. In this case, the output bounding boxes are discretized into a set of already predefined boxes using various aspect ratios and scales, locating the position on the feature map. The prediction of the network yields scores for each category of object in each default box, and it produces adjustments to the box to better match the shape of the object. Thus, within the one-stage methods studied, the most interesting and traditionally used, which can be highlighted, are YOLO [22]. This method is responsible for solving the object detection problem by applying a regression to bounding boxes that are separated in space, together with the application of class probabilities associated with the boxes.