# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 3 Results and Discussion
### Experiment Results and Discussion
#### 3.2.1 Experiment 1: FCAW-GMAW Weld Seam  
While it is possible to differentiate between FCAW and GMAW welding types at first glance, this task is difficult for those who are not trained in welding. For this reason, we have proposed the use of an automated system that can detect the type of welding used to create a particular welding bead. For the present dataset [47], the weld seams were extracted from the plates illustrated in Figure 4, thereby ensuring that the model was trained on a labeled image predominantly containing the bead to be detected. The images in this dataset exhibit minimal additional space beyond the bead itself, and although the dimensions are standard (\(320\times 320\)), the appearance is irregular, as evidenced in Figure 9, which presents a set of these images. To address this challenge, two distinct classes of objects were identified for detection: FCAW and GMAW. As illustrated in the initial row of Table 2, the performance metrics demonstrate a consistent performance above 98% across all metrics. The test set of the mAP metric registered at 93%, which might have influenced the model's convergence time. However, it was observed that 100 epochs were sufficient for the model to reach convergence. The anticipated outcomes can be evaluated. Notwithstanding the presence of imbalance in the dataset, as demonstrated in Table 3, it is contended that the sample size is sufficient for the model to attain optimal performance. Figure 10 shows the results after training of the first experiment, FCAW-GMAW detection. The graphs illustrate the training loss for the bounding box, segmentation, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Proportion**} & \multirow{3}{*}{**Data**} & \multicolumn{2}{c}{**Experiment 1**} & \multicolumn{2}{c}{**Experiment 2**} & \multicolumn{3}{c}{**Experiment 3**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Classes**} & \multicolumn{2}{c}{**Classes**} & \multicolumn{2}{c}{**Classes**} & \multicolumn{2}{c}{**Classes**} \\ \cline{3-10}  & & **FCAW** & **GMAW** & **BAD** & **GOOD** & **GOOD** & **LOP** & **UNDER** & **OP** \\ \hline \multirow{3}{*}{80\%} & Train set & \multirow{3}{*}{1190} & \multirow{3}{*}{513} & \multirow{3}{*}{1182} & \multirow{3}{*}{507} & \multirow{3}{*}{564} & \multirow{3}{*}{313} & \multirow{3}{*}{539} & \multirow{3}{*}{322} \\  & 90\% & & & & & & & & \\ \cline{1-1} \cline{2-10}  & Val set & \multirow{3}{*}{10\%} & \multirow{3}{*}{57} & \multirow{3}{*}{131} & \multirow{3}{*}{56} & \multirow{3}{*}{62} & \multirow{3}{*}{35} & \multirow{3}{*}{60} & \multirow{3}{*}{36} \\  & 10\% & & & & & & & & \\ \hline
20\% & Test set & \multirow{3}{*}{331} & \multirow{3}{*}{142} & \multirow{3}{*}{329} & \multirow{3}{*}{141} & \multirow{3}{*}{156} & \multirow{3}{*}{87} & \multirow{3}{*}{150} & \multirow{3}{*}{90} \\  & & & & & & & & & \\ \hline
100\% & Total data & \multirow{3}{*}{1653} & \multirow{3}{*}{712} & \multirow{3}{*}{1642} & \multirow{3}{*}{704} & \multirow{3}{*}{782} & \multirow{3}{*}{435} & \multirow{3}{*}{749} & \multirow{3}{*}{448} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of labels classified by training, validation, and test sets is presented according to the number of classes in each experiment. Figure 9: Set of images of the FCAW-GMAW dataset, where the predicted label and the percentage of that prediction can be observed. An irregular character of the image content can be observed, where the welding bead occupies practically all the space of the image. classification, and confidence score predictions. The x-axis denotes the training epochs, with the y-axis representing the loss value (no units shown). At approximately 75 training epochs, a significant decrease is observed in all graphs, indicating that the model effectively learned to identify the weld bead. This is further confirmed by the decrease in loss related to the bounding box, segmentation, classification, and confidence score predictions.