## 4 Results and Discussions
### Ablation Studies  
In order to verify the effectiveness of the proposed modules, ablation experiments are conducted. The results are shown in Table 2. We take the GA-RetinaNet [25] with ResNet-50 [21] and FPN [34] as our baseline.  
Firstly, ACM (without MFAF) is applied to GA-RetinaNet. We could see that the AP increases from 35.5% to 36.1%. Meanwhile, the \(AP_{S}\) and \(AP_{M}\) is improved by 4.1% and 1.0% respectively. This result indicates that the modeling capability of CNN can be enhanced by adjusting the location and shape of the convolution unit, especially for small defects. However, we also find that \(AP_{L}\) drops by 0.6% since a large receptive field is important for detecting large defects. However, the receptive field of the shape offsets in ACM is too small for large defects. We believe that this is the main reason for the dropping of \(AP_{L}\).  
Then, MFAF is used in ACM to adaptively integrate multi-scale features. With MFAF, the AP is improved by 0.5%. Meanwhile, we could also find that the \(AP_{S}\), \(AP_{M}\) and \(AP_{L}\) is 1.9%, 0.2% and 2.1% better than before respectively. The result demonstrates that MFAF contributes to improving the surface defect detection performance for all surface defects with different sizes.  
Finally, AAM is leveraged to replace original GA [25]. As illustrated in Table 2, the use of AAM shows a significant increase from 36.6% to 37.3%. This result better proves our previous views: _The shape of an anchor closely relates to its location, and the location information could help AAM yield more suitable anchors. In addition, the \(AP_{75}\) is improved by 0.8%. Theses improvements further demonstrate that AAM could yield more suitable anchor boxes for defects. We could also find that the \(AP_{S}\) and \(AP_{M}\) are improved by 1.5% and 0.7%. However, it also worth noting that \(AP_{L}\) drops by 1.2%. We argue that the offsets in AAM have too-small receptive fields, which compromises our ability to locate large objects.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Alias** & **IoU** & **Area** & **Meaning** & & & & \\ \hline \(AP\) & 0.50–0.95 & all & & \(AP\) at IoU thresholds from 0.50 to 0.95 & & & \\ \(AP_{50}\) & 0.50 & all & & \(AP\) at 0.50 IoU threshold & & & \\ \(AP_{75}\) & 0.75 & all & & \(AP\) at 0.75 IoU threshold & & & \\ \(AP_{S}\) & 0.50–0.95 & small & & \(AP\) for small objects: \(area<32^{2}\) & & & \\ \(AP_{M}\) & 0.50–0.95 & medium & & \(AP\) for medium objects: \(32^{2}<area<96^{2}\) & & \\ \(AP_{L}\) & 0.50–0.95 & large & & \(AP\) for large objects: \(area>96^{2}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ACM** & **MFAF** & **AAM** & \(AP\) & \(AP_{\mathbf{50}}\) & \(AP_{\mathbf{75}}\) & \(AP_{\mathbf{S}}\) & \(AP_{M}\) & \(AP_{L}\) \\ \hline  & & & 0.355 & 0.728 & 0.290 & 0.231 & 0.283 & 0.437 \\ ✓ & & & 0.361 & 0.736 & 0.311 & 0.272 & 0.293 & 0.431 \\ ✓ & ✓ & & 0.366 & 0.732 & 0.324 & 0.291 & 0.295 & 0.452 \\ ✓ & ✓ & ✓ & 0.373 & 0.746 & 0.332 & 0.306 & 0.302 & 0.440 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The effects of proposed module.