# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 4 Conclusions and Future Work  
This paper presents the findings of an investigative study conducted to detect fillet weld beads in a series of 2D images, which were also captured during the course of this study. The images were subjected to a rigorous treatment process, resulting in the creation of multiple datasets. These datasets were then used to conduct a series of experiments aimed at detecting various types of welds, assessing the quality of the weld bead fabrication, and identifying defects. The object detection process has been focused using the YOLOv8  
Figure 14: Training curves and performance metrics for the YOLOv8 object detection model, where we try to detect correctly made weld seams, without any defects (labeled as GOOD), and weld seams with some manufacturing defect, labeling and classifying several of these most common defects (labeled as UNDER for Undercuts, LOP for Lack Of Penetration, and OP for Other problems). The x-axis shows the training epochs, while the y-axis shows the loss values, both without units. The curves show the learning of the model; it is observed that the loss is significant, although somewhat milder than in the two previous experiments. Same case as in the precision, recall, and mAP50 scores that, although lower than before, we can deduce that the training has been effective. Figure 13: Plate of fillet weld beads analyzed with the model obtained in experiment 3. It shows three of the four types of weld beads (objects) for which the model of this experiment has been trained. In addition, the image shows other elements that the model is able to discard. algorithm, with appropriate configuration of its hyperparameters and application of a specific methodology developed for this study. Thus, after the three experiments carried out, it is demonstrated that the YOLOv8 algorithm is effective for the detection of filled weld beads, since a prediction performance of over 97% is achieved in all the characteristics studied. However, as the complexity of detection has increased, with more challenging classes to detect or a greater number of class detections greater than two in the same experiment; performance has exhibited a notable decline. Nevertheless, there appears to be considerable potential for enhancing the performance of the model by developing a system capable of detecting a series of weld beads with multiple characteristics. Consequently, it can be deduced that this iteration of the algorithm is also efficacious in detecting smaller objects that differ from those typically identified by the algorithm. This work signifies the genesis of the study of welding seam detection by implementing a geometric deep learning model with the YOLOv8 algorithm. The study encompassed two binary detection experiments and one multiclass detection experiment, which have been exhaustively delineated and deliberated. The outcomes substantiate the efficacy of the approach, signifying the potential for further research in this domain. It is evident that further refinement and expansion of this research are possible, as the scenarios can be rendered more complex to assess the efficacy of the algorithm in question. Additionally, conducting a wider range of experiments that encompass additional classes, such as welding defects, would further augment the dataset and facilitate more comprehensive investigation. A potential limitation of the present study, akin to other related works on object detection, is the possibility that the images obtained from another system and subsequently inferred by the model trained here may exhibit defective detection or yield performance that is lower than that attained in this study. This is despite the implementation of the same methodology and precautions during image capture. Consequently, we propose the utilization of Transfer Learning techniques, grounded in the findings of this study, to identify additional types of welding beads produced by diverse processes.