# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 4 Results and Discussion
### Experiment 3 and Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{3}{c}{**AP\({}_{\textbf{50}}\) (\%)**} & \multicolumn{2}{c}{**FPS**} \\ \cline{3-7}  & & **Crack** & **Deposition** & **Root** & **Stagger** & **(frame/s)** \\ \hline SSD [14] & 83.4 & 70.5 & 85.5 & 88.4 & 89.4 & 44 \\ Faster R-CNN [2] & 79.8 & 66.2 & 81.2 & 82.6 & 89.1 & 8 \\ YOLOv3 [6] & 85.4 & 76.8 & 90.6 & 80.5 & 93.5 & 20 \\ Improved YOLOv3 [3] & 88.1 & 75.2 & 90.7 & 93.8 & 92.8 & 12 \\ YOLOv7 & 88.3 & 80.1 & 87.1 & 91.7 & 94.5 & 14 \\ YOLOv8 & 87.0 & 73.6 & 88.3 & 91.1 & 95.1 & 14 \\ DETR [29] & 86.7 & 75.5 & 91.3 & 88.6 & 91.1 & 13 \\ Swin-Trans-YOLOv4 [30] & 78.2 & 54.6 & 89.3 & 80.0 & 88.8 & 9 \\ Ours & 92.3 & 88.0 & 91.8 & 94.2 & 95.2 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of the different models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Recall (\%)**} & \multicolumn{4}{c}{**Average (\%)**} \\ \cline{2-7}  & **Crack** & **Deposition** & **Root** & **Stagger** & **Recall** & **Precision** & **F1** \\ \hline SSD [14] & 73.5 & 84.5 & 82.9 & 91.1 & 83.0 & 76.2 & 79.5 \\ \hline Faster R-CNN [2] & 83.8 & 86.9 & 85.4 & 96.2 & 88.1 & 49.0 & 62.8 \\ \hline YOLOv3 [6] & 70.6 & 85.7 & 74.4 & 91.1 & 80.5 & 82.5 & 81.5 \\ \hline Improved YOLOv3 [3] & 69.1 & 83.3 & 89.0 & 93.7 & 83.8 & 80.8 & 81.8 \\ \hline YOLOv7 & 77.9 & 78.6 & 81.7 & 87.3 & 81.4 & 90.9 & 85.5 \\ YOLOv8 & 72.1 & 82.1 & 85.3 & 98.7 & 84.5 & 79.5 & 81.7 \\ DETR [29] & 79.4 & 90.4 & 91.4 & 94.9 & 89.0 & 57.9 & 70.0 \\ Swin-Trans-YOLOv4 [30] & 33.8 & 79.7 & 70.7 & 87.3 & 67.8 & 83.5 & 73.0 \\ \hline Ours & 83.8 & 88.1 & 90.2 & 93.7 & 89.0 & 90.1 & 89.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recall, Precision and F1 values of different models on the test set. pipe junction, and its color is very similar to the background of sewer, and its features are not obvious, which causes the model to miss the detection. It can be seen that when encountering some extreme situations, such as uneven lighting or noise interference during image shooting or transmission, the detection effect will be significantly affected. In addition, when the defect features are not obvious or very similar to the pipeline background, all of them may cause error cases. Among them, errors in identifying crack defects are relatively common. The attention mechanism can improve the ability of the model to extract critical features and suppress the influence of interference information such as noise. Introducing an attention mechanism in the model may reduce the occurrence of these errors, and further research will be done in the future. Figure 12: Precision–recall curves for different models: (**a**) P–R curve for crack; (**b**) P–R curve for deposition; (**c**) P–R curve for root; and (**d**) P–R curve for stagger. Figure 13: Examples of false detected defects. (**a**) crack misidentified as deposition; (**b**) crack misidentified as stagger; (**c**) crack was not detected.