## 4 Discussion
### Discussion of Ablation Experimental Results  
To balance algorithm performance and inference efficiency, this study implemented lightweight pruning on the backbone network. While solely compressing the model's parameters, the mAP\({}_{50}\) decreased to 0.756, with a slight improvement in the recall rate to 0.690. The parameter size was reduced to 62% of the baseline, and the inference speed increased by 54.4%. This indicates that although simple parameter pruning enhanced the detection efficiency, it significantly decreased the backbone's feature representation capability. Consequently, this approach necessitates further integration with other improved modules to enhance the feature extraction capacity. As presented in Tables 1 and 2, replacing the CBS module with SPDDEConv as the backbone's down-sampling method improved the model's mAP\({}_{50}\) to 0.817 and recall rate to 0.743. Specifically, the mAP\({}_{50}\) for defects with weak features but certain edge information, such as scratches and edge breakage, increased by 20.9% and 6.5%, respectively. These results indicate that SPDDEConv, through its spatial partitioning and channel stacking approach, preserved more information from the original feature maps and enhanced the weak texture features of defects by introducing difference convolution methods. The heatmaps in Figure 13c,e,g further demonstrate that the model's attention to elongated defects, like scratches, significantly improved after integrating SPDDEConv. Moreover, the introduction of HDC and VDC branches during down-sampling provided the algorithm with prior knowledge, enabling it to directionally extract edge information of defects. This design also contributed to a notable acceleration in the model's convergence speed during training, as depicted in Figure 14.  
Among all seven types of defects, oil pollution, chalk marks, and cracks generally covered larger areas, while edge breakage tended to be smaller in size. Spot-like defects and shavings exhibited significant scale variations, with defect areas ranging from the size of a grain of rice to that of a palm. Moreover, different types of defects could coexist, making the scale change of defects in the image more complicated. To address this issue, this study introduced the ShareSepHead and C2f_SAC modules to enhance the model's ability to detect multi-scale defects. The design concept of ShareSepHead is similar to NAS-FPN [39]. By reusing parameters, it inserts convolutional layers with shared weights into the detection head to fuse knowledge from feature maps of different scales, enabling cross-layer knowledge transfer and enhancing the model's ability to detect multi-scale defects. After adding ShareSepHead alone, the model's parameters remained at 9.8 M, while the mAP\({}_{50}\) increased to 0.828 and the recall rate jumped to 0.779. Notably, the mAP\({}_{50}\) for spot-like defects and shavings, two defect types with significant scale variations, improved by 10.6% and 20.8%, respectively, demonstrating significant enhancement effects. The C2f_SAC module, through its integrated SAC component, looked twice at the input features with different atrous rates, and the outputs were combined by switches. This design expanded the receptive field while enhancing the C2f module's ability to extract multi-scale defect features. According to the ablation experimental results, adding C2f_SAC alone led to a slight performance drop of 1%. However, when C2f_SAC was combined with ShareSepHead or SPDDEConv, the mAP\({}_{50}\) significantly improved to over 0.84. This indicates that while atrous convolutions can expand the receptive field, they rely on richer feature information. The addition of C2f_SAC contributed to a further performance improvement only when the backbone had strong feature extraction capabilities and could extract sufficiently rich features. Regarding inference speed, the \"Slim\" strategy adopted in this study reduced the inference time by 54.4% (from 3.15 ms to 2.04 ms) through halving the number of channels in the backbone network.