## 5 Discussion  
The proposed YOLO-MAD framework demonstrates significant improvements in detection accuracy and robustness on both NEU-DET and GC10-DET datasets compared to recent state-of-the-art methods. By integrating three purpose-built modules--AKConv, BiFPN, and Detect_DyHead--YOLO-MAD addresses key challenges in industrial surface defect detection, including irregular geometry, multi-scale heterogeneity, and background \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **P** & **R** & **mAP50** & **mAP50-95** \\ \hline YOLOv8n & 0.646 \(\pm\) 0.002 & 0.678 \(\pm\) 0.003 & 0.712 \(\pm\) 0.002 & 0.373 \(\pm\) 0.001 \\ +AKConv & **0.688**\(\pm\) 0.003 & 0.689 \(\pm\) 0.003 & 0.725 \(\pm\) 0.002 & 0.385 \(\pm\) 0.004 \\ +BiFPN & 0.665 \(\pm\) 0.002 & **0.712**\(\pm\) 0.002 & 0.738 \(\pm\) 0.005 & 0.398 \(\pm\) 0.001 \\ +Detect\_DyHead & 0.672 \(\pm\) 0.003 & 0.708 \(\pm\) 0.003 & **0.745**\(\pm\) 0.001 & **0.410**\(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: **Impact of Individual Module Integration on YOLOv8n Performance. ** This ablation study quantifies the contribution of each proposed module (AKConv, BiFPN, Detect_DyHead) to the overall detection accuracy of the YOLOv8n baseline model. Bold values indicate the best performance for each metric within the respective ablation row. Note that each “+” here refers to the individual addition of the improvement on the YOLOv8n model, not a cumulative addition. Results are presented as mean \(\pm\) standard deviation over three independent runs, conducted to ensure the statistical robustness of the findings. interference. The quantitative results shown in Tables 1 and 2 validate the performance advantages of each component when handling different types of defects, outperforming baseline YOLOv8n/YOLOv11n and showing competitive or superior results against recent advanced detectors like MSFT-YOLO [40] and YOLO-BA [46]. First, YOLO-MAD achieves superior performance on the IN, PA, and SC categories in the NEU-DET dataset, which often feature clear boundaries and structured shapes. These results can be largely attributed to the AKConv module. By incorporating deformable convolutional kernels and dynamic sampling, AKConv enhances the model's sensitivity to local spatial irregularities and fine-grained features. Compared with standard convolution used in YOLOv8n and YOLOv11n, AKConv provides more adaptive receptive fields tailored to defect contours, thereby improving detection of irregular and small-scale patterns. AKConv also presents considerable potential for further enhancement. Its detection performance is sensitive to parameters such as kernel shape and sampling strategy, making it relatively unstable under varying conditions. However, if its design is adapted to task-specific and even sample-specific characteristics, its adaptability can be substantially improved. Moreover, the integration of attention mechanisms can increase its generalization capability. For instance, placing a pretrained feature extractor before AKConv to guide the construction of irregular receptive fields may offer a promising optimization strategy. Second, BiFPN plays a vital role in fusing multi-scale features, especially for defects with significant size variation or unclear boundaries, such as PA and RS. With its bidirectional flow and learnable fusion weights, BiFPN allows semantic features from different depths to be dynamically reweighted and aggregated, thereby enhancing informative cues while suppressing irrelevant ones. This design contributes to YOLO-MAD's strong generalization across scale-diverse targets, a key advantage over methods lacking sophisticated multi-scale fusion. In future work, BiFPN could be extended with context-aware routing or graph-based attention modules to further improve its effectiveness in complex scenarios. Third, Detect_DyHead greatly improves robustness in noisy and ambiguous environments.