## 5 Experiment Results and Analysis  
### Ablation Experiments  
To verify the effectiveness of the proposed improvement strategies, a set of comparative ablation experiments were conducted. The experiments took YOLOv8n as the baseline model, and the results are presented in Table 2. In the table, C2f_DCN, EVC, and WIoU represent the three proposed improvement points in this study. The symbol \(\surd\) denotes the adoption of the improvement point.  
According to the ablation experiment results in Table 2, the first group directly employed YOLOv8n with an mAP value of 76.4%. In the second group, the C2F_DCN module network replaced the C2F module in the backbone. This enhancement allowed the model to strengthen the fusion of multi-level features and effectively adjust the positions and sizes of receptive fields, enabling more flexible handling of complex surface defects on steel materials, which optimized the detection accuracy of the model, with the mAP value increasing to 78.7%. The third group embedded the EVC module into the backbone network of YOLOv8n, and the model fused the displayed explicit visual center of CFPNet to capture the long-range dependence of features and improve the detection of small targets. The mAP value of the model was improved by 1.5%, indicating that the EVC module could effectively improve the model's detection accuracy. The fourth group replaced the loss function of YOLOv8n with the WIoU loss function. The number of parameters of the model and the amount of computation were basically unchanged, and the accuracy slightly improved, but the detection speed of the model was improved by 18%, which proves that the addition of WIoU can make the model more accurate in localization and classification. The fifth group added the EVC module to the second group to enhance the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sequence** & **C2f\_DCN** & **EVC** & **WIoU** & **mAP @ 0.5** & **FPS** & **Params (M)** & **GFLOPs** \\ \hline
1 & & & & 76.4 & 132 & 3.01 & 8.2 \\
2 & \(\surd\) & & & 78.7 & 104 & 3.16 & 7.6 \\
3 & & \(\surd\) & & 77.9 & 101 & 7.29 & 11.6 \\
4 & & & \(\surd\) & 77.0 & 156 & 3.01 & 8.2 \\
5 & \(\surd\) & \(\surd\) & & 79.6 & 96 & 7.45 & 11.0 \\
6 & \(\surd\) & \(\surd\) & \(\surd\) & 80.3 & 91 & 7.45 & 11.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment results.  
model's adjustment and optimization for different feature scales. This allowed the model to better capture both global information and local details across different scales. Although adding the EVC module resulted in an increase in Params and GFLOPs, the experimental results prove that the improvement effectively increased the detection accuracy, resulting in an mAP value of 79.6%. Finally, the sixth group replaced the original loss function with a WIoU loss function based on the fifth group, which reduced the difference in gradient gain between the high-quality and low-quality samples, strengthened the model's localization performance, and improved its generalization ability, with an mAP value of 80.3%.  
The sixth experiment's minimum frame rate was 91 frames per second, which is within an acceptable range and can satisfy the real-time needs of industrial inspection, even though each improvement may somewhat lower the FPS. The above ablation experiments demonstrate the effectiveness of the three improvement points and also show the contribution that each one makes to the model's detection effectiveness.