## 5 Results and Analysis
### First-Stage Recognition
#### 5.2.4 The Results of Ablation Experiments  
An ablation experiment is similar to the control variate method, which is an intuitive way to study causality. As we saw in the previous section, the improved YOLOv5 model in this paper leads to a certain degree of performance improvement. In order to verify whether the three improvements are all effective at improving performance, and whether there is any interaction between them, we conducted ablation experiments on the Enriched-NEU-DET dataset. We use four schemes to represent different improvements to YOLOv5. The improvement schemes are shown in Table 5.  
\"ECA module\" refers to the embedded attention mechanism ECA module on the C3 module of the backbone network, which is marked with a purple square in Figure 6; \"Scale 4 layer\" refers to the model that adds the new feature fusion layer to YOLOv5, which is marked with a green background in Figure 6; \"Generate 24 \(\times\) 24 feature maps\" removes the Conv and C3 layer that obtained 1/32 scale feature information, replacing it with a Conv and C3 layer that extracts feature information at a 1/24 scale. * Scheme 1 is the original YOLOv5 mode;
* Scheme 2 refers to the model that further applies ECA attention mechanism to the model;
* Scheme 3 refers to the model that further adds the new feature fusion layer Scale 4 layer;
* Scheme 4 is the Improved-YOLOv5 model proposed in this paper. The final test results of the four schemes are shown in Table 6. We use six letters to refer to the six types of surface defects: a represents \"crazing,\" b represents \"inclusion,\" c represents \"patches,\" d represents \"pitted_surface,\" e represents \"rolled-in_scale,\" and f represents \"scratches.\" \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{8}{c}{**AP (\%)**} & \multicolumn{8}{c}{**Recall (\%)**} & \multirow{2}{*}{**mAP (\%)**} \\ \cline{2-13} \cline{5-13}  & **a** & **b** & **c** & **d** & **e** & **f** & **a** & **b** & **c** & **d** & **e** & **f** \\ \hline
1 & 52.4 & 70.6 & 83.6 & 78.8 & 61.8 & 78.7 & 53.6 & 71.1 & 87.0 & 72.4 & 53.8 & 71.6 & 71.0 \\
2 & 52.8 & 72.2 & 84.7 & 83.9 & 63.3 & 80.1 & 54.1 & 74.5 & 87.1 & 76.9 & 60.3 & 74.8 & 72.9 \\
3 & 53.1 & 76.9 & 86.6 & 93.0 & 66.9 & 84.3 & 56.2 & 78.8 & 88.9 & 79.9 & 68.7 & 79.0 & 76.8 \\
4 & 54.6 & 79.6 & 86.1 & 92.3 & 66.1 & 89.6 & 57.7 & 80.9 & 88.9 & 79.6 & 67.9 & 81.4 & 78.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison results of four schemes. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Schemes**} & **ECA Module** & **Scale 4 Layer** & **Generate 24 \(\times\) 24 Feature Maps** & **Generate 18 \(\times\) 18 Feature Maps** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\
2 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\
3 & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The improved schemes. From Scheme 1 in Table 6, the mAP value of the original YOLOv5 is 71.0%. The mAP of Scheme 2 is 72.9%, which shows that the embedding of the ECA module has a certain positive impact on the network. Next, we tested Scheme 3. It shows that the new feature fusion layer works and contributes to defect detection. In particular, the recognition accuracy of \"pitted_surface\" defects increased by 9.1%, which reflects that most of the \"pitted_surface\" defects in the dataset are small-scale defects, and also reminds us that the steel production process needs to pay special attention to the occurrence of this type of defects. Finally, we tested the model in this paper and found that the performance of the overall model had been further improved. For instance, the mAP value increased by 1.3%.