# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
## 4 Experimental Results and Analysis
### Results Comparison Experiment
Figure 9 demonstrates that YOLOv8-WD's recall rate consistently surpasses that of YOLOv8, reflecting a superior ability to capture positive samples. According to Figure 10 and Table 2, YOLOv8-WD shows a 3% improvement in mAP@50 over YOLOv8 and exhibits a stable convergence of the curve. This stability indicates that YOLOv8-WD possesses robust performance, good generalization capabilities, and an effective training strategy. Consequently, YOLOv8-WD proves to be more valuable in object detection tasks, particularly in scenarios requiring high reliability and consistency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **F1** \\ \hline YOLOv5 & 80.5\% & 83.1\% & 87.8\% & 81.8\% \\ YOLOv8 & 85.2\% & 83.5\% & 87.5\% & 84.3\% \\ YOLOv10 & 87.1\% & 60.0\% & 65.9\% & 70.8\% \\ YOLOv8 \(-\) WD & 86.4\% & 85.9\% & 90.5\% & 86.1\% \\ YOLOv8 + BiFPN & 86.0\% & 81.5\% & 87.0\% & 83.7\% \\ YOLOv8 + iRMB & 84.3\% & 82.9\% & 87.8\% & 83.6\% \\ YOLOv8 + SCConv & 84.3\% & 82.5\% & 87.8\% & 83.4\% \\ YOLOv8 + SWC & 84.1\% & 83.7\% & 87.8\% & 83.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different algorithms. Figure 8: Comparison of accuracy curve. Figure 9: Recall curve comparison. Figure 11 shows that YOLOv8-WD's box, dfl, and classification loss curves are similar to YOLOv8 on the training set, indicating comparable learning and stability. On the validation set, YOLOv8-WD exhibits smoother Box and DFL loss curves, suggesting greater robustness. However, its slightly higher loss values indicate that the added complexity in YOLOv8-WD results in increased loss due to more features being processed. This does not necessarily reflect worse performance, and additional metrics are needed for a full assessment. Figure 11: Loss curve comparison. Figure 10: mAP@50 curve comparison. Figure 12 compares the defect detection results between YOLOv8 and YOLOv8-WD by defect category. YOLOv8 performed well on dents and scratches with mAP@50 scores of 96.30% and 91.40%, respectively, but less effectively on bubbles and slag, with scores of 75.30% each. YOLOv8-WD showed similar performance on indentations and scratches but improved detection of bubbles and slag, with mAP@50 increasing by 1.7% and 13.6%, respectively. Thus, YOLOv8-WD effectively mitigates dataset class imbalance, reducing average precision gaps and achieving more balanced performance. Figure 13 presents a test set sample containing four types of welding defects: pits, slag, scratches, and bubbles. For pits and slag, the original algorithm demonstrates satisfactory detection performance, with the improved algorithm yielding a slight increase in mAP@50. However, the original algorithm exhibits suboptimal detection performance for scratches and bubbles. As observed in Figure 13c, the original algorithm suffers from the missed detection of scratches. The improved algorithm addresses this issue, significantly enhancing detection accuracy. Furthermore, the improved algorithm also shows a notable improvement in bubble detection accuracy, with mAP@50 increasing by approximately 10%, representing a substantial advancement. Figure 12: Comparison of mAP@50 for different defects on the validation set before and after improvements. Figure 13: _Cont._