# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
## 4 Experimental Results and Analysis
### Results Comparison Experiment  
To further validate the effectiveness of the proposed algorithm, experiments were conducted using the same dataset described previously. The study compared the improved algorithm with current state-of-the-art approaches, specifically utilizing YOLOv5, YOLOv8, and YOLOv10 for comparative analysis. Additionally, enhancements were introduced to the YOLOv8 architecture by incorporating modules such as BiFPN, iRMB, SCConv, and SWC. The improved model was then compared to YOLOv8-WD. The comparison primarily focused on evaluating the performance of these object detection models using mAP@50 and F1 score, two key metrics for performance assessment. Based on Table 2, among the baseline models, YOLOv8 demonstrated an improvement over YOLOv5 in precision but a slight drop in recall. Despite the slight recall drop, YOLOv8's F1 score (84.3%) is higher than YOLOv5's F1 score (81.8%), indicating that YOLOv8 offers a better balance between precision and recall. The [EMAIL_ADDRESS] of Yolov5 was slightly higher than that of Yolov8, but the difference was not much more than 0.3%, meaning that the two performed similarly in the overall accuracy of the detection targets. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **EfficientViT** & **CAFM** & **DynamicConv** & **[EMAIL_ADDRESS]** & **F1** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & 87.5\% & 84.3\% \\
2 & \(\surd\) & \(\times\) & \(\times\) & 88.9\% & 81.2\% \\
3 & \(\times\) & \(\surd\) & \(\times\) & 87.1\% & 79.0\% \\
4 & \(\times\) & \(\times\) & \(\surd\) & 87.6\% & 84.0\% \\
5 & \(\surd\) & \(\surd\) & \(\times\) & 88.9\% & 83.3\% \\
6 & \(\times\) & \(\surd\) & \(\surd\) & 87.6\% & 83.1\% \\
7 & \(\surd\) & \(\times\) & \(\surd\) & 88.7\% & 83.5\% \\
8 & \(\surd\) & \(\surd\) & \(\surd\) & 90.5\% & 86.1\% \\ \hline \end{tabular}
\end{table} 
Table 1: Ablation experiments. YOLOv10 had the highest accuracy (87.1%) across all models, but its recall rate declined substantially (60.0%), significantly lower than that of YOLOv5 and Yolov8. The low recall rate severely affected its [EMAIL_ADDRESS] (65.9%) and F1 score (70.8%). This indicates that, although YOLOv10 performs well in precision, it fails to detect a large number of related targets and is therefore less reliable in applications that require high recall rates. Overall, YOLOv10 shows an imbalance between accuracy and recall that has a negative impact on its overall effectiveness. For the enhanced models, YOLOv8-WD showed the overall best performance with the highest [EMAIL_ADDRESS] (90.5%) and highest F1 score (86.1%) across all models. The precision and F1 fraction of YOLOv8+BiFPN were improved, but the recall rate was slightly lower than that of the basic YOLOv8 model by 2.0%. YOLOv8+iRMB maintained a similar balance to Yolov8, with a slight decrease in precision of 0.9%, but a higher recall rate than base YOLOv8, with a good final F 1 score (83.6%) and [EMAIL_ADDRESS] (87.8%). YOLOv8+SCConv performed nearly as well as Yolov8+iRMB with the same precision as [EMAIL_ADDRESS] but with a slightly lower recall rate and F1 score than Yolov8+iRMB. YOLOv8+SWC provided a robust balance, with a high recall rate (83.7%) and an F1 score (83.9%) superior to some other YOLOv8 variants. In conclusion, while YOLOv10 achieves the highest precision, it is clear that YOLOv8-WD offers the most robust and balanced performance, making it the ideal choice in scenarios requiring both high precision and recall. Variants of YOLOv8 show specific improvements depending on architectural changes, but none outperform YOLOv8-WD in overall effectiveness. As illustrated in Figure 8, YOLOv8-WD initially exhibits lower accuracy compared to YOLOv8 but surpasses it in later stages of training. However, the accuracy curve for YOLOv8-WD does not stabilize smoothly towards the end, indicating that while the model's performance improves over time, it does not achieve a steady increase in accuracy.