# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 3 Experiment and Results Analysis
### Analysis of Experimental Results
#### 3.4.4 Detection Results  
To enhance the realism of the on-site environment simulations, this study includes FPS comparative experiments using an Intel NUC Mini PC featuring an i5-1240P processor. The corresponding experimental results are presented in Table 5.  
As indicated in Table 5, the YOLOv5-Sewer model demonstrates a notably superior detection speed compared to other models when deployed on the Intel NUC Mini PC with i5-1240P. Consequently, the lightweight model proposed in this article exhibits outstanding performance on portable devices.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{8}{c}{**AP0.5/\%**} & \multirow{2}{*}{**FLOPs/G**} & \multirow{2}{*}{**Param/M**} & \multirow{2}{*}{**Size/MB**} & \multirow{2}{*}{**mAP/\%**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{6-13}  & **1** & & & & & & & & & & & & & \\ \hline a & 70.1 & 86.1 & 70.3 & 80.7 & 84.9 & 62.3 & 79.2 & 79.1 & 370 & 137 & 9.1 & 76.6 & 12 \\ b & 70.6 & 88.4 & 70.1 & 79.3 & 83.2 & 65.4 & 81.5 & 78.3 & 12.9 & 8.7 & 17.8 & 77.1 & 163 \\ c & 71.6 & 90.4 & 73.2 & 86.4 & 86.4 & 60.1 & 87.2 & 70.7 & 63 & 26.3 & 6.3 & 78.3 & 33.7 \\ d & 72.0 & 92.1 & 83.0 & 90.9 & 94.0 & 72.1 & 92.7 & 77.3 & 13.2 & 6.03 & 12.3 & 84.3 & 52 \\ e & 77.8 & 96.1 & 80.4 & 92.8 & 88.9 & 69.8 & 90.2 & 76.4 & 5.1 & 3.14 & 6.7 & 84.0 & 112 \\ \hline \hline \end{tabular}  
* 1–8 represent cracks, utility intrusion, obstacles, joint offset, debris, holes, buckling, and roots, respectively; a–e represent Faster-RCNN, YOLOv3-tiny, SSD, YOLOv7-tiny, and YOLOv5-Sewer, respectively.  
\end{table}
Table 4: Comparison of experimental results.  
The performance of the YOLOv5-Sewer model is validated on the Intel NUC Mini PC with i5-1240P and compared with other models, using the test set derived from the merged dataset. To evaluate the model's general applicability and robustness, images in the test set underwent adjustments in brightness and hue to simulate scenarios with inadequate lighting conditions. The training, validation, and testing sets included 1130, 269, and 268 images. The test results are depicted in Figure 8.  
There are a total of 10 rows of images in Figure 8, with each two rows representing the detection results of a model. The first and second rows depict the detection results of the original image and the image under dim conditions, respectively. As shown in the image in column 7, there are underwater cracks. The dim condition image intentionally diminishes the brightness and tone of the original image. Based on the outcomes of the sewer defect detection, the YOLOv5-Sewer model can successfully identify eight types of sewer defects with a low miss rate, even in dimly lit conditions. Consequently, the YOLOv5-Sewer model exhibits strong performance in tasks related to sewer defect detection. Therefore, YOLOv5-Sewer successfully achieves model lightweight with acceptable accuracy loss. The YOLOv5-Sewer model is more suitable for rapid defect detection on portable on-site devices.  
In conclusion, the YOLOv5-Sewer model exhibits superior performance in the realm of sewer defect detection, boasting several advantages. These include a diverse range of categories, enabling the identification of eight types of sewer defects, such as cracks, utility intrusion, obstacles, joint offset, debris, holes, buckling, and roots. Additionally, the model showcases a fast detection speed, lightweight design, and acceptable accuracy on low-performance devices. Its universal applicability and robustness are evident as it successfully detects defects under dim conditions.  
Figure 8: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **FPS** \\ \hline Faster-RCNN & 0.5 \\ SSD & 3.5 \\ YOLOv5s & 8 \\ YOLOv7-tiny & 9 \\ YOLOv3-tiny & 9 \\ YOLOv5-Sewer & 12 \\ \hline \hline \end{tabular} Model name represents the name of the defect detection model used, and FPS represents frames per second.  
\end{table}
Table 5: Comparative experiments on the Intel NUC Mini PC with i5-1240P.