## 4 Experiment
### Performance Comparison with SOTA Approaches
(**a**) Original images are shown. (**b**) Predictions made by YOLOv8n. (**c**) Predictions generated by YOLO-MAD. Rows are organized from top to bottom as follows: (1) CR, (2) IN, (3) PA, (4) PS, (5) RS, and (6) SC defects. To obtain a finer insight into our model's classification performance at the class level, we analyzed the confusion matrices for both the YOLOv8n baseline and our proposed YOLO-MAD model, as presented in Figure 12 and Figure 13, respectively. A visual inspection reveals that the majority of correct defect identifications in the YOLO-MAD model exhibit deeper color intensities along the diagonal, indicating a general enhancement in the recognition accuracy for most defect types. This indicates that the modifications introduced in YOLO-MAD have broadly improved the model's ability to correctly classify various steel surface defects. Figure 11: **PR curves for the proposed YOLO-MAD model**, showcasing its enhanced detection capabilities. Figure 10: **Precision–recall (PR) curves for the YOLOv8n model**, illustrating its detection performance across various thresholds. However, certain challenges persist. Notably, the accuracy for CR defects appears to have decreased. Furthermore, a small number of IN samples, which were previously correctly classified by the baseline, are now misclassified into categories that were not erroneous before. A more detailed examination of the CR row in the YOLO-MAD confusion matrix highlights a significant issue: a substantial portion of CR defect instances are mistakenly identified as background. In fact, the proportion of CR defects misclassified as background often exceeds the proportion of CR defects correctly identified. This phenomenon strongly indicates that the structural similarity between crazing defects and the background's texture and color distribution poses a considerable challenge for precise feature extraction and subsequent accurate classification. For the majority of defect categories, Figure 14 Grad-CAM visualizations reveal that the YOLO-MAD model directs significantly increased emphasis on the precise details of the defects [43]. The concentrated red areas in the heatmaps often align well with the morphological traits of the defects. For instance, in SC and IN defects, the red regions clearly trace their respective linear or irregular geometric structures, indicating that the  
Figure 12: The **confusion matrix for the YOLOv8n model**, visually representing its classification performance. Each row indicates the true class, while each column represents the predicted class, with diagonal values showing correctly classified instances. Figure 13: The **confusion matrix for the proposed YOLO-MAD model**, illustrating its enhanced classification accuracy across different defect types. model effectively learns and leverages these distinct features for accurate identification. This enhanced focus on relevant defect features contributes to the overall improved performance observed in our model [44,45]. However, for certain defects like CR, the Grad-CAM heatmaps show a less concentrated and more dispersed red activation. This suggests that when the structural features of the defect are highly similar to the surrounding background environment, the model struggles to precisely pinpoint the discriminative regions. This difficulty in correctly selecting  
Figure 14: Comparison of prediction localization using Grad-CAM heatmaps. Grad-CAM (Gradient-weighted Class Activation Mapping) generates visual explanations highlighting the image regions that are most important for the model’s classification decision. In these heatmaps, redder and deeper colored areas indicate higher activation weights, providing a qualitative insight into the model’s focus. (**a**) Original images; (**b**) YOLOv8n Grad-CAM heatmaps; (**c**) YOLO-MAD Grad-CAM heatmaps. From top to bottom, rows display: (1) CR, (2) PS, (3) SC, (4) RS, (5) PA, and (6) IN defects.