# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 3 Results and Discussion
### Experiment Results and Discussion
#### 3.2.3 Experiment 3: Good-Lop-Under Weld Seam  
In order to carry out a more complex experiment, which could detect more than two classes and, at the same time, have an importance within the field we are investigating, we created this third experiment. The objective of this experiment was to conduct a preliminary study of the defects that may arise during the manufacturing process of weld beads, with a focus on identifying two of the most prevalent issues: undercuts and lack of penetration. This approach led to the establishment of an additional category for the detection of other welding problems, thereby distinguishing these welds from those that are well-manufactured. Consequently, a methodology was devised to detect a correctly manufactured weld, or the presence of defects, such as undercuts (UNDER), lack of penetration (LOP), and other issues (OP), in conjunction with the class of correct welds (GOOD).  
It is acknowledged that there are other significant welding defects that warrant consideration, including but not limited to slag inclusions, porosity, incomplete fusion, spatter, overlap, and cracks. In this article, we sought to conduct a series of experiments (experiments 1 and 2) employing binary object detection, encompassing two distinct yet essential themes in welding operations. The third experiment was selected as a case study to demonstrate that, given the exposure of all experiments and methodologies, objects (veld beads) can be detected for more than two detection classes utilizing the same algorithm.  
As illustrated in the second row of Table 2, the performance metrics for this second experiment demonstrate consistent high performance, similar to the results observed in the initial experiment. Notably, the precision and recall metrics exhibit higher values, accompanied by a decline in the validation set of the \"good\" class. This observation suggests that the model may encounter challenges in accurately classifying this category. The performance metrics for the remaining mAP metrics closely mirror those observed in prior experiments.An examination of the number of samples in each of the four classes, as presented in Table 3, reveals that the 'LOP' class contains the smallest number of samples. This could potentially result in the lowest performance in the mAP metric of the validation set; however, further analysis is necessary to ascertain the validity of this hypothesis.  
In this third experiment, a dataset [49] was created for the purpose of detecting four distinct objects (GOOD-UNDER-LOP-OP), which correspond to well-manufactured welds (the first class) and various defects (the remaining three classes). Figure 13 illustrates a plate containing multiple fillet weld seams, each meticulously labeled with its respective model.  
In this third experiment, a dataset was created for the purpose of detecting four distinct objects (GOOD-UNDER-LOP-OP), which correspond to well-manufactured welds (the first class) and various defects (the remaining three classes). Figure 2 illustrates a plate containing multiple fillet weld seams, each meticulously labeled with its respective model.  
As illustrated in Figure 14, the training curves and performance metrics of the model trained in this third experiment demonstrate non-uniform trends, particularly in the initial epochs, where the model experiences difficulty adjusting the loss. This difficulty may be attributed to the model's task of detecting four distinct classes, a challenge exacerbated by the limited number of samples available.The model achieves effective learning through a total of 300 epochs. As in the previous experiment, the learning stabilized after epoch 200 and reached maximum efficiency. As in the preceding experiment, an analysis of the loss revealed that the curves required more epochs to demonstrate effective training for bounding box, segmentation, classification, and confidence score predictions. However, a sufficient number of epochs were used in this experiment, as in the previous ones, to avoid overfitting.