## 4 Discussion  
### Discussion of Comparative Experimental Results  
From the results of the comparative experiment, it is evident that earlier algorithms exhibited relatively inferior performance. Faster R-CNN failed to converge without the use of the pre-trained weights, and its convergence speed was faster when the pre-trained weights were used, but the final mAP\({}_{50}\) remained low at only 0.702. The YOLOv5s algorithm, under the same parameter settings, triggered early stopping during the later stages of training due to the inability to further improve performance. In terms of accuracy, the PBD-YOLO algorithm achieved an mAP\({}_{50}\) of 0.856, representing a 5.5% improvement over the baseline algorithm, YOLOv10s, and outperforming the RT-DETR and RTMDet series detection algorithms. Compared to the earlier YOLOv5s algorithm, it showed a 12% improvement, surpassing the enhancement methods based on YOLOv5s reported in [11; 14; 38]. For the more stringent mAP\({}_{50-95}\) metrics, the PBD-YOLO algorithm surpassed all the algorithms with an accuracy of 0.609. These results demonstrate that the proposed algorithm exhibited stronger robustness and detection capabilities across different IoU values.  
Meanwhile, PBD-YOLO was capable of detecting seven types of defects, including spot-like defects, shavings, oil pollution, edge breakage, chalk marks, scratches, and cracks. Compared with the literature [11; 12; 14; 15], the detection of scratches and edge breakage, which are more difficult to recognize, was added, and the mAP\({}_{50}\) values of the newly added defects all surpassed 0.9.  
In practical applications of particleboard surface defect detection, production factories generally prioritize defect detection rates over classification accuracy. In such scenarios, the recall metric becomes the crucial performance indicator. The proposed PBD-YOLO  
Figure 13: Eigen-CAM heatmap: (**a**) original image, (**b**) baseline, (**c**) baseline + Slim, (**d**) baseline + Slim + ShareSepHead, (**e**) baseline + Slim + SPDDEConv, (**f**) baseline + Slim + C2f_SAC, (**g**) baseline + Slim + ShareSepHead + SPDDEConv, (**h**) baseline + Slim + SPDDEConv + C2f_SAC, (**i**) baseline + Slim + ShareSepHead + C2f_SAC, and (**j**) PBD-YOLO.  
algorithm achieved a recall rate of 0.814, surpassing the baseline YOLOv10s algorithm by 13%. This significant improvement effectively reduced the probability of false negatives, better aligning with industrial quality control requirements. As evidenced by the detection examples depicted in Figure 12, PBD-YOLO demonstrated superior localization accuracy and confidence compared to mainstream defect detection algorithms, particularly for challenging defect types. The algorithm exhibited enhanced performance in detecting low-contrast defects (e.g., scratches) and small-scale defects, with detection results more similar to ground truth.  
The proposed PBD-YOLO algorithm demonstrated remarkable parameter efficiency with 14.1 M parameters, representing an 11.3% reduction compared to YOLOv10s (with 15.9 M parameters), while maintaining comparable inference speed to the baseline. In comparison with other algorithms, although RTMDet-s had a substantial parameter value of 39.03 M, its mAP\({}_{50}\) was 0.836 and inference speed was 54.20 ms, which are both inferior to those of PBD-YOLO. This demonstrates that the algorithm proposed in this study achieved a better balance between accuracy and efficiency through structural optimization. Furthermore, while RT-DETR-ResNet50 exhibited a higher recall rate, where the value was 0.776, its large parameter size (86.1 M) and high computational cost (the time was 8.10 ms) make it challenging to deploy for real-time high-performance applications on resource-constrained industrial edge-computing devices.