# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 3 Experiment and Results Analysis
### Analysis of Experimental Results
#### 3.4.3 Contrast Experiments  
In order to further validate the reliability of the YOLOv5-Sewer model for sewer defect detection, comparative experiments are conducted against popular object detection algorithms, including Faster-RCNN, YOLOv3-tiny [36], SSD, YOLOv7-tiny [37], and YOLOv5-GBC [17]. The experimental results, summarized in Table 4, highlight the performance of YOLOv5-Sewer in comparison to these algorithms.  
Below are the key results of YOLOv5-Sewer compared to Faster-RCNN, YOLOv3-tiny, SSD, and YOLOv7-tiny on this dataset.  
1. Faster-RCNN: * mAP improved by 7.4%; * Detection speed increased by 100 FPS; * Floating-point operations decreased by 98.6%; * Parameters decreased by 97.7%; * Model size decreased by 26.4%.
2. YOLOv3-tiny: * mAP improved by 6.9%; * Detection speed decreased by 51 FPS; * Floating-point operations decreased by 60.5%; * Parameters decreased by 63.9%;  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{4}{c}{**Module**} & \multirow{2}{*}{**FLOPs/G**} & \multirow{2}{*}{**Param/M**} & \multirow{2}{*}{**Size/MB**} & \multirow{2}{*}{**mAP/\%**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{6-9}  & **C3-Faster** & & & & & & & & \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 5.9 & 3.53 & 7.5 & 80.3 & 136 \\
2 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 4.8 & 2.97 & 6.4 & 82.3 & 147 \\
3 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 4.9 & 3.03 & 6.5 & 83.4 & 126 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 5.1 & 3.14 & 6.7 & 83.7 & 112 \\
5 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 5.1 & 3.14 & 6.7 & 84.0 & 112 \\ \hline \hline \end{tabular} \(\surd\) indicates the adoption of this module; \(\times\) indicates the non-adoption of this module.  
\end{table}
Table 3: Fusion experiment results.  
* Model size decreased by 62.3%.  
* mAP improved by 5.7%;
* Detection speed increased by 78.3 FPS;
* Floating-point operations decreased by 91%;
* Parameters decreased by 88%;
* Model size increased by 6%.  
* mAP decreased by 0.3%;
* Detection speed increased by 60 FPS;
* Floating-point operations decreased by 61.4%;
* Parameters decreased by 47.9%;
* Model size decreased by 45.5%.  
Due to the different datasets and device models used, YOLOv5-Sewer and YOLOv5-GBC are compared in terms of lightweight indicators. The specific results are as follows:  
* Floating-point operations decreased by 58.2%;
* Parameters decreased by 73.9%;
* Model size decreased by 85.4%.  
Taking into account the lightweight metrics and mean Average Precision (mAP), the YOLOv5-Sewer model achieves a lightweight design with acceptable accuracy trade-offs. It demonstrates improvements over Faster-RCNN and SSD in terms of mAP and detection speed, while also outperforming YOLOv3-tiny and Yolov7-tiny in terms of floating-point operations, parameters, and model size. Although YOLOv3-tiny has the highest detection speed, it occupies more resources and has lower accuracy. The comprehensive evaluation suggests that YOLOv5-Sewer is well suited for sewer defect detection, offering a favorable trade-off between lightweight characteristics and detection accuracy.