## 3 Experiment and Results
### Results of Ablation Experiment  
This study introduced several improved modules, including ShareSepHead, C2f_SAC, and SPDDEConv, into the baseline algorithm, YOLOv10. Additionally, a \"Slim\" strategy, by halving the number of channels in the backbone network, was proposed to optimize the model's efficiency. To verify the effectiveness and performance of these enhancements, this study conducted ablation experiments by incrementally adding or replacing the improved schemes within the algorithm, quantitatively analyzing the impact of these improvements on the overall performance. The ablation experiment results are presented in Table 2, where A is the baseline and B-I represent the experimental results obtained by applying different improvement modules. As presented in Table 2, the model performance exhibited varying trends with the introduction of different improved modules. After incorporating the \"Slim\" operation, the model's parameters decreased to 9.8 M, and the inference time decreased to 2.04 ms. When SPDDEConv was added, the mAP\({}_{50}\) increased from 0.756 to 0.817, and the recall increased from 0.690 to 0.743, while the parameters rose to 11.5 M and the inference time increased to 2.18 ms. With the introduction of C2f_SAC, the recall increased from 0.690 to 0.731, accompanied by an increase in the inference time to 2.84 ms. When all improvement modules were integrated, the model achieved its best performance, with an mAP\({}_{50}\) of 0.856, an mAP\({}_{50-95}\) of 0.609, and a recall of 0.814. Figure 11: Comparative experimental curves of different algorithms. Figure 12: Comparative experimental visualization results of different algorithms. To evaluate the performance of each improved module on specific defect categories, this study has compiled and presents the precise mAP\({}_{50}\) and recall metrics for each algorithm from the ablation experiment, as presented in Tables 3 and 4. After introducing the SPDDEConv, Algorithm D demonstrated significant improvements in detecting scratch defects, with the mAP\({}_{50}\) increasing from 0.756 to 0.914 and recall rising from 0.694 to 0.829. Upon the addition of C2f_SAC, Algorithms E and H showed improved performance in detecting shavings, achieving mAP\({}_{50}\) values of 0.819 and 0.828, respectively. Furthermore, Algorithm G, which integrated both SPDDEConv and C2f_SAC, achieved notable advancements in detecting both shavings and scratches, with mAP\({}_{50}\) scores reaching 0.905 and 0.831. Among all algorithms, the mAP\({}_{50}\) values for \"oil pollution\" defects were consistently low, with a baseline value of only 0.452 before any improvements. PBD-YOLO (specifically Algorithm I) enhanced this metric to 0.573 by integrating multiple improvement modules, representing a 34.2% increase compared to baseline Algorithm \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Defect Class**} & \multicolumn{8}{c}{**Recall**} \\ \cline{2-11}  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** \\ \hline Spot-like defects & 0.595 & 0.770 & 0.785 & 0.770 & 0.754 & 0.803 & 0.689 & **0.852** & 0.820 \\ Shavings & 0.588 & 0.571 & 0.801 & 0.667 & 0.762 & 0.714 & **0.902** & 0.762 & 0.759 \\ Oil pollution & 0.295 & 0.375 & 0.425 & 0.276 & 0.381 & 0.339 & 0.46 & 0.446 & **0.503** \\ Edge breakage & 0.900 & 0.833 & 0.933 & 0.933 & 0.906 & **0.967** & 0.933 & **0.967** & 0.933 \\ Chalk marks & 0.750 & 0.750 & 0.732 & 0.786 & 0.821 & 0.826 & 0.807 & 0.786 & **0.857** \\ Scratches & 0.773 & 0.694 & 0.829 & 0.829 & 0.657 & 0.771 & 0.830 & 0.794 & **0.883** \\ Cracks & 0.889 & 0.833 & **0.944** & **0.944** & 0.833 & **0.944** & **0.944** & **0.944** & **0.944** \\ \hline \end{tabular} Bold text indicates that the algorithm performs best in this metric.  
\end{table} 
Table 4: Comparison of recall of different types of defects.