## 4 Experiment
### Performance Comparison with SOTA Approaches
From a model standpoint, this paper suggests that the issue is related to the \"non-optimal control\" of variability in the experiments. Specifically, the design of ConvAK has considerable autonomy in terms of kernel shape, size, and stride configuration, and changes in any of these parameters can affect the recognition performance of all categories. While the initial kernel we generate did improve the overall MAP and the detection of specific types such as Cg and Wf, the targeted design of the kernel shape for these features may not ensure adequate feature coverage during x and y coordinate transformations for irregularly extended variations of creases in the horizontal, vertical, or lateral directions. This, in turn, leads to a decline in the detection capability for this category. As shown in Figure 9, for several defect types, including IN, PA, and RS, the YOLOMAD model consistently exhibited higher confidence scores in its predictions compared to the baseline. This improvement underscores the effectiveness of our multi-scale feature learning and fusion strategies, primarily facilitated by components like BiFPN and the adaptive capabilities of Detect_DyHead. By integrating and processing information across various scales more effectively, the model has acquired a richer understanding of diverse defect characteristics, leading to more robust and confident detections. Furthermore, a notable improvement was observed for SC defects. In instances where the YOLOv8n baseline failed to detect certain scratches, YOLO-MAD successfully identified them. This enhanced capability can be attributed to the AKConv module, which is designed to learn and analyze multiple geometric structural features simultaneously. By adapting its convolutional kernels to capture the unique linear characteristics of scratches alongside other defect morphologies, the model achieves a more comprehensive perception of the defect's geometry, enabling the detection of subtle or previously missed imperfections. Conversely, for CR defects, the YOLO-MAD model occasionally displayed lower prediction confidence. This challenge likely stems from the inherent similarity between the visual characteristics of crazing defects and the background, both in terms of color and texture distribution, on the steel surface. When defect features closely resemble the surrounding environment, even advanced models may struggle to precisely delineate the defect boundaries and confidently classify them. This suggests a potential area for future research focusing on enhancing feature discriminability in visually ambiguous scenarios. To further measure the detection proficiency of our proposed YOLO-MAD model, we analyzed its PR curves in comparison to the YOLOv8n, as presented in Figures 10 and 11. Overall, the PR curve for \"all cases\" of the YOLO-MAD model demonstrates superior performance compared to the baseline, indicating the enhanced general capability of our model. This improvement is consistently reflected across the PR curves for the majority of individual defect types, where YOLO-MAD shows a clear upward trend, signifying better precision at various recall levels. This suggests that the integrated modules effectively contribute to more accurate and comprehensive defect detection across most categories. However, a notable exception is observed for CR defects, where the model's performance, as depicted by its PR curve, shows a declining trend. This degradation is likely attributed to the challenges previously discussed, specifically the inherent similarity between the visual characteristics of crazing defects and the background's color and texture distribution on the steel surface. Such visual ambiguity can hinder the precise extraction and fusion of geometric features, hindering the model's ability to precisely distinguish these defects from their surroundings. This highlights a specific area where further refinement in feature learning for highly ambiguous defect types is warranted. Figure 9: Comparison of predictive outputs across various defect categories.