## 4 Experiments and Result Analyses
### Experimental Results
#### 4.4.2 Performance Improvement by Each Module  
Figure 15 shows that after introducing each module based on DeepLab v3+, the detected boundaries are nearer the boundaries in the ground truth than before the modules were applied, indicating that each module successfully improved the detect accuracy. Table 2 shows that after importing each module in turn, F1 score and IoU improved greatly compared with those using DeepLab v3+. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Wheel Hub**} & \multicolumn{3}{c}{**Tile**} & \multicolumn{3}{c}{**Fabric**} & \multicolumn{3}{c}{**Weld**} \\ \cline{2-11}  & **F1** & **AP** & **IoU** & **F1** & **AP** & **IoU** & **F1** & **AP** & **IoU** & **F1** & **AP** & **IoU** \\ \hline SegNet & 0.748 & 0.863 & 60.6 & 0.643 & 0.935 & 51.8 & 0.762 & 0.906 & 64.7 & 0.426 & 0.675 & 27.1 \\ Unet & 0.757 & 0.936 & 61.6 & 0.772 & 0.943 & 64.6 & 0.847 & 0.924 & 73.7 & 0.591 & 0.761 & 41.9 \\ DeepLab v3+ & 0.783 & 0.942 & 64.8 & 0.709 & 0.942 & 57.9 & 0.807 & 0.908 & 69.0 & 0.564 & 0.752 & 39.2 \\
**Ours** & **0.845** & **0.963** & **73.2** & **0.844** & **0.954** & **73.9** & **0.858** & **0.944** & **75.3** & **0.729** & **0.783** & **57.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: F1 score, AP, and IoU (%) of different networks on four defect datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{8}{c}{(a) Improvement of F1 Score} \\ \hline \multirow{2}{*}{Method} & \multicolumn{2}{c}{Wheel hub} & \multicolumn{2}{c}{Tile} & \multicolumn{3}{c}{Fabric} & \multicolumn{3}{c}{Weld} \\ \cline{2-11}  & F1 & \(\Delta\)(\%) & F1 & \(\Delta\)(\%) & F1 & \(\Delta\)(\%) & F1 & \(\Delta\)(\%) \\ \hline DeepLab v3+ & 0.783 & - & 0.709 & - & 0.807 & - & 0.564 & - \\ +HRNet & 0.8 & **1.7\(\uparrow\)** & 0.782 & **7.3\(\uparrow\)** & 0.847 & 4.0\(\uparrow\) & 0.689 & **12.5\(\uparrow\)** \\ +ASPP with sum & 0.805 & **0.5\(\uparrow\)** & 0.816 & **3.4\(\uparrow\)** & 0.852 & 0.5\(\uparrow\) & 0.726 & **3.7\(\uparrow\)** \\ +Decouple and Decoder & 0.845 & **4.0\(\uparrow\)** & 0.844 & **2.8\(\uparrow\)** & 0.858 & 0.6\(\uparrow\) & 0.729 & **0.3\(\uparrow\)** \\ \hline \hline \end{tabular}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{Method} & \multicolumn{3}{c}{Wheel hub} & \multicolumn{3}{c}{Tile} & \multicolumn{3}{c}{Fabric} & \multicolumn{3}{c}{Weld} \\ \cline{2-11}  & IoU(\%) & \(\Delta\)(\%) & IoU(\%) & \(\Delta\)(\%) & IoU(\%) & \(\Delta\)(\%) & IoU(\%) & \(\Delta\)(\%) \\ \hline DeepLab v3+ & 64.8 & - & 57.9 & - & 69 & - & 39.2 & - \\ +HRNet & 66.4 & **1.6\(\uparrow\)** & 65.1 & **7.2\(\uparrow\)** & 73.8 & **4.8\(\uparrow\)** & 52.6 & **13.4\(\uparrow\)** \\ +ASPP with sum & 67.6 & **1.2\(\uparrow\)** & 70.2 & **5.1\(\uparrow\)** & 74.7 & **0.9\(\uparrow\)** & 57 & **4.4\(\uparrow\)** \\ +Decouple and Decoder & 73.2 & **5.6\(\uparrow\)** & 73.9 & **3.7\(\uparrow\)** & 75.3 & **0.6\(\uparrow\)** & 57.3 & **0.3\(\uparrow\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Improvement of F1 score and IoU by each module. Figure 15: On the wheel hub defect dataset, performance improvement by each module on the basis of DeepLab v3+. (a) defect maps of DeepLab v3+; (b) detect maps after using HRNet; (c) detect maps after using ASPP with sum; and (d) detect maps after using body and edge decoupling module and decoder module. The red boxes are the defect positions, the white boxes are the mistake positions, and the yellow boxes are the boundary positions of the defect. The defects of the tile and weld defect datasets have a more irregular appearance and larger scale changes than those of the other datasets. Using HRNet and introducing ASPP with sum greatly improved F1 score and IoU, verifying that HRNet can improve the feature resolution and ASPP with sum helps extract multi-scale features and solve the gridding effect. In the wheel and tile defect datasets, the appearance of the defect boundary is more similar to the normal area than the other datasets.