## 4 Experiment
### Performance Comparison with SOTA Approaches  
To thoroughly assess the performance enhancements and lightweighting achieved by the YOLO model following module modifications in this study, we used two key indicators, namely, mean average precision (mAP) and the number of floating-point operations (GFLOPs). These quantitative measures provide a multidimensional assessment of the model's effectiveness. Average precision (AP) is computed as the integral of the precision-recall curve, and its calculation formula is as follows:  
\[AP=\int_{0}^{1}p(r)\,dr, \tag{12}\]  
Here, the precision-recall function \(p(r)\) denotes the precision at recall level \(r\). The integration operation effectively combines precision measurements across the entire spectrum of recall, offering a robust and balanced assessment of the model's ability to correctly identify instances of a specific class while minimizing false detections. To obtain a comprehensive evaluation across all object categories, the mean average precision (mAP) is obtained by averaging the individual AP scores over all \(N_{c}\) object categories, calculated as follows:  
\[mAP=\frac{1}{N_{c}}\sum_{i=1}^{N_{c}}AP_{i}, \tag{13}\]  
Floating-point operations (GFLOPs), representing giga floating-point operations, quantify a model's computational complexity. In practical industrial applications, particularly on edge devices or in scenarios demanding high real-time performance, a model's computational efficiency is paramount. By comparing the GFLOPs of the model before and after modifications, we can quantify the improvement in model lightweighting. This study aims to effectively decrease the model's computational burden while simultaneously enhancing detection performance through module modifications. Therefore, GFLOPs is a key indicator to verify the achievement of our model's lightweighting objective. Table 2 shows that WSS-YOLO [21] achieves the highest overall mAP of 82.3% on the NEU-DET dataset. Nevertheless, our proposed YOLO-MAD demonstrates highly competitive performance, attaining a mAP of 76.6%. This result significantly outperforms all other baseline YOLO models by a considerable margin, highlighting the effectiveness of its architectural design. Our model also secures the best performance in half of the defect categories, that is, IN with 85.0%, PA with 94.2%, and SC with 93.0%. These strong results are largely attributed to the AKConv module, which excels at capturing the distinct geometric structural features of these defect types, often characterized by their clear and relatively larger shapes. Regarding computational efficiency, YOLO-MAD maintains computational frugality (9.4 GFLOPs, 4.27 M parameters), placing it firmly in the lightweight model category suitable for real-time applications. Compared to the official baselines, \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Image Count** & \begin{tabular}{c} **Defect** \\ **Categories** \\ \end{tabular} & **Resolution** & \
\begin{tabular}{c} **Data Split** \\ **(Train:Test)** \\ \end{tabular} &
\begin{tabular}{c} **Annotation** \\ **Type** \\ \end{tabular} \\ \hline NEU-DET [35] & 1800 & 6 & 200 \(\times\) 200 & 4:1 & Bounding \\  & & & (80\%:20\%) & Box \\ GC10-DET [37] & 2300 & 10 & 2048 \(\times\) 1000 & 4:1 & Bounding \\  & & & & (80\%:20\%) & Box \\ \hline \hline \end{tabular}
\end{table} 
Table 1: **Dataset summary and comparison. ** This table details key characteristics of the NEU-DET and GC10-DET datasets, including image count, number of defect categories, image resolution, data splitting, and annotation type, providing a comprehensive overview of the datasets used in this study. YOLO-MAD delivers this substantial performance gain with only a modest increase in computation over YOLOv8n (8.1 GFLOPs) and remains vastly more efficient than larger variants like YOLOv8l (164.8 GFLOPs). However, YOLO-MAD shows lower performance than WSS-YOLO on defects like crazing (CR).