# A Lightweight Method for Detecting Sewer Defects Based on Improved YOLOv5
## 3 Experimental Results and Analysis
### Comparison Analysis with Other Models  
To further verify the effectiveness of the improved model, this paper compares YOLOv5-GBC with mainstream object detection models under the same experimental conditions. Other detection models include single-stage detection models SSD, YOLOv3, YOLOv4, and YOLOv7, the dual-stage detection model Faster-RCNN, the anchor-free detection model YOLOX, and YOLOv5-M, in which the original YOLOv5 backbone network is replaced with the MobileNet network. The experimental results are shown in Table 6.  
As shown in Table 6, the YOLOX model achieved the highest mAP, but its model volume and FLOPs are higher, making it unsuitable for mobile devices. The two-stage network Faster RCNN has the highest recall rate, but its detection accuracy is poor due to its lower precision, and its model volume and FLOPs are the largest, making it the slowest in terms of detection speed and unsuitable for detection tasks on mobile platforms. The single-stage network YOLO series models have good detection accuracy, but their volume and computation are large, and they cannot meet the lightweight deployment requirements on mobile devices. Compared with other models, the improved model, YOLOv5-GBC, proposed in this paper has significant advantages in terms of model volume and FLOPs, and its mAP is only lower than that of the optimal YOLOX model, while recall rate and F1 value are comparable to other models; thus, it can meet the high precision requirements of pipeline defect detection tasks. Overall, the model proposed in this paper has lower model volume and computation while maintaining high detection accuracy, making it more suitable for small mobile devices with limited computing resources.  
In order to verify the defect detection ability of different models, we selected five representative models to conduct experiments under the same conditions, and the detection results are shown in Figure 11. It can be seen that our improved model has the ability to accurately detect different types of defects in complex environments and is comparable to the detection results of the YOLOX. Compared with other detection models, it has better detection results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **[EMAIL_ADDRESS]** & \multirow{2}{*}{**Recall/\%**} & \multirow{2}{*}{**Precision/\%**} & \multirow{2}{*}{**F1**} & \multicolumn{2}{c}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**FLOPs/10\({}^{9}\)**} \\  & \(\prime\)**\%** & & & & & \(\prime\)**10\({}^{6}\)** & & \\ \hline SSD & 82.38 & 80.79 & 75.87 & 0.78 & 24.01 & 91.6 & 115.97 \\ YOLOv3 & 84.57 & 80.31 & 85.18 & 0.83 & 61.54 & 234.76 & 65.62 \\ YOLOv4 & 86.76 & 85.17 & 86.32 & 0.86 & 63.95 & 243.96 & 59.98 \\ YOLOX & 89.89 & 89.78 & 86.04 & 0.87 & 54.15 & 206.57 & 65.78 \\ YOLOv7 & 88.34 & 81.41 & 90.9 & 85.5 & 37.21 & 141.95 & 44.42 \\ Faster RCNN & 79.79 & 88.07 & 49.02 & 0.63 & 136.75 & 521.66 & 252.66 \\ YOLOv5-M & 85.61 & 81.29 & 84.08 & 0.82 & 22.63 & 86.34 & 18.05 \\ YOLOv5-GBC & 87.21 & 82.43 & 85.38 & 0.84 & 12.06 & 46.01 & 12.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of detection results of different object detection models.