## 4 Experiment
### Performance Comparison with SOTA Approaches
This limitation primarily stems from CR defects often exhibiting features (color, texture) highly similar to the background. In such cases with low feature contrast, the AKConv module faces challenges in precisely extracting and delineating the subtle spatial structures characteristic of CR from the noisy background. As presented in Table 3, YOLO-MAD continues to demonstrate strong generalization, achieving a robust mAP of 68.4% on the more diverse GC10-DET dataset. This performance surpasses all compared baseline YOLO models (YOLOv8n/l, YOLOv11n/l) as well as traditional detectors like Libra Faster R-CNN and RetinaNet, validating its capability across varied defect scenarios. WSS-YOLO [21] maintains the highest overall mAP on this dataset at 72.0%, maintaining a relatively small gap. YOLO-MAD exhibits high-level performance across multiple defect types, securing the best scores for Cg at 96.0% and Wf at 77.1%. These results underscore YOLO-MAD's robustness and effectiveness in handling diverse and complex defect patterns prevalent in industrial steel surfaces. Despite YOLO-MAD's overall exceptional performance, a slight decrease in accuracy is observed for several specific defect types (such as Os, Ss, and Cr). This indicates that there is still room for improvement. From a data perspective, the decline is attributed to the unique characteristics of these defects or their relatively low representation in the \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **mAP** & **CR** & **IN** & **PA** & **PS** & **RS** & **SC** & **GFLOPs** \\ \hline SSD [38] & 63.8 & 47.3 & 68.5 & 88.6 & 68.4 & 54.7 & 55.0 & 281.9 \\ YOLO-BA [39] & 74.8 & 36.3 & 67.8 & 91.0 & **96.6** & **70.6** & 86.4 & - \\ MSFT-YOLO [40] & 75.2 & 56.9 & 80.8 & 93.5 & 82.1 & 52.7 & 83.5 & - \\ WSS-YOLO [21] & **82.3** & **58.1** & 80.9 & 93.9 & 94.2 & 73.1 & **93.9** & 7.7 \\ YOLOv8n & 71.2 & 43.3 & 78.8 & 92.4 & 83.6 & 48.8 & 80.1 & 8.1 \\ YOLOv11n & 71.1 & 49.3 & 79.5 & 92.2 & 80.2 & 60.6 & 65.0 & **6.3** \\ YOLOv8l & 72.5 & 52.0 & 75.1 & 90.7 & 84.4 & 55.0 & 77.6 & 164.8 \\ YOLOv11l & 71.6 & 41.2 & 80.4 & 92.4 & 81.0 & 56.4 & 78.2 & 86.6 \\ YOLO-MAD & 76.6 & 38.5 & **85.0** & **94.2** & 83.3 & 65.5 & **93.0** & 9.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance assessment of various models on the NEU-DET defect dataset. Note that the models YOLOv8n, YOLOv8l, YOLOv11n, and YOLOv11l in the table are the official versions without additional modules. (The bold numbers indicate that this method performs the best in this category of defects). \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Method** & **mAP** & **Pu** & **WI** & **Cg** & **Ws** & **Os** & **Ss** & **In** & **Rp** & **Cr** & **Wf** \\ \hline Libra Faster R-CNN [41] & 58.8 & **99.5** & 42.9 & 94.9 & 72.8 & **72.1** & 62.8 & 18.8 & **37.4** & 17.6 & 69.3 \\ RetinaNet [42] & 65.5 & 79.6 & 91.5 & 94.3 & 79.1 & 62.0 & **66.4** & 29.7 & 33.9 & 35.2 & 77.0 \\ WSS-YOLO [21] & **72.0** & 98.2 & **95.2** & 93.5 & **87.3** & 57.9 & 62.8 & 38.0 & 35.4 & **58.1** & **93.4** \\ YOLOv8n & 63.6 & 98.2 & 92.2 & 90.4 & 80.5 & 69.9 & 61.4 & 31.3 & 8.4 & 32.6 & 71.0 \\ YOLOv8l & 66.1 & 96.9 & 94.0 & 91.8 & 86.5 & 69.5 & 58.2 & 38.8 & 17.4 & **38.0** & 70.2 \\ YOLOv11n & 63.1 & 95.8 & 89.5 & 90.1 & 81.6 & 68.8 & 57.9 & **39.4** & 5.6 & 28.7 & 73.2 \\ YOLOv11l & 66.9 & 96.4 & 95.1 & 93.3 & 83.9 & 71.5 & 62.5 & 39.3 & 22.1 & 34.7 & 70.7 \\ YOLO-MAD & 68.4 & 98.6 & 93.4 & **96.0** & 80.7 & 68.7 & 56.5 & 38.6 & 25.2 & 33.5 & 77.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance assessment of various models on the GC10-DET defect dataset. **Notations:** Defect types include punching, weld line, crescent gap, water spot, oil spot, scratches, inclusion, rolled pit, crease, and wrinkle, which are abbreviated as PU, WI, Cg, Ws, Os, Ss, In, Rp, Cr, and Wf, respectively. (The bold numbers indicate that this method performs the best in this category of defects). dataset.