# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 4 Results and Discussion
### Experiment 3 and Results  
In order to further verify the effectiveness of the final improved model, we compared it with current mainstream object detection models with the same training method. Other detection models include single-stage detection models SSD, YOLOv3, YOLOv7 and YOLOv8, two-stage detection models Faster-RCNN and transformer-based model, DETR [29] and Swin-Trans-YOLOv4. The Swin-Trans-YOLOv4 model is to use Swin Transformer [30] as the backbone of YOLOv4. The experimental results are shown in Table 6, and Figure 11 shows some detection results of these detection models. As shown in Table 6, YOLOv4-D-SPP3 achieves a mAP of 92.3%, which is higher than other recent studies and achieves the highest AP on all classes of detection. It should be noted that due to the more complex network structure and more computing costs, the FPS of our model is lower than other models, but still higher than Faster R-CNN. We selected five representative models to detect some defects, and the detection results are shown in Figure 11, the red boxes are stagger, the purple boxes are root, the blue boxes are crack and the green boxes are deposition. All five models have the ability to detect different types of defects in complex environments, but the detection results are different. The model proposed in this paper can detect more defects under the same conditions and has the highest degree of overlap between the predicted bounding box and ground truth, which means that our model has better detection performance. Nevertheless, all other models have missed or wrong detections. For example, the detection results of YOLOv3 have different degrees of missed detection for stagger and cracks on the first and third \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{4}{c}{**AP\({}_{50}\) (\%)**} & \multirow{2}{*}{**FPS (Frame/s)**} \\ \cline{2-2} \cline{5-6}  & & **Crack** & & & **Deposition** & **Root** & **Stagger** \\ \hline YOLOv4-DIoU & 87.9 & 80.8 & 87.9 & 91.8 & 91.0 & 13 \\ YOLOv4-CIoU & 87.7 & 83.5 & 86.6 & 91.6 & 89.0 & 13 \\ YOLOv4-C-SPP3 & 88.2 & 79.2 & 88.7 & 91.1 & 93.4 & 12 \\ YOLOv4-D-SPP3 & 92.3 & 88.0 & 91.8 & 94.2 & 95.2 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of the original and improved models. images. The YOLOv7 model has missed detection for stagger and root on the second and fourth images. The detection result of Faster R-CNN has many redundant prediction boxes on the second image and low overlap with ground truth on the fourth image. The detection result of DETR model have many redundant detections on the third and fourth pictures, which will greatly interfere with the judgment of technicians. Figure 11: Some detection results of fewer defect images using different models: (**a–d**) show the detection results by YOLOv3, YOLOv7, Faster R-CNN and DETR, respectively; (**e**) shows the detection results by our model. In conclusion, the improved model proposed in this paper, namely YOLOv4-D-SPP3, can more effectively detect several different types of defects and accurately label their locations. In terms of accuracy, our model achieves the best performance among these methods. It should be pointed out that our method may be wrong in some special cases. As shown in Figure 13, the crack in Figure 13a is incorrectly identified as deposition. The reason may be that the sewer pipe at this location is darker and not smooth like normal pipes, which are very similar to deposition defects, resulting in model detection errors. In Figure 13b, the crack is incorrectly identified as a stagger. The reason may be that the image is very blurry and contains a lot of noise, which causes significant interference to the detection of the model. In Figure 13c, the crack defect is missed. The location of the crack is indicated by the red arrow in the figure. The reason may be that this crack occurs at the