## 4 Results and Discussion
### Ablation Experiments  
This section compares the results of YOLOv5-BiFC with different modules added. By demonstrating the ability of this method to compress model size and achieve more effective recognition results through various combinations, as shown in Table 4, it can be observed that the network layer parameters with the addition of the C3 Ghost module exhibit significant differences in computational power and model size. The model size of YOLOv5-BiFC is 7.8 MB with 3.8 M parameters, reducing the original model size by 44.2%. Figure 15: Detection results of different detectors, including YOLOv5, YOLOv6, YOLOv7-tiny, YOLOv8, where (**a**) represents images universally recognizable by all detectors, and (**b**) represents images where the model trained with improvements shows better target recognition compared to other models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **P(\%)** & **R(\%)** & **F1(\%)** & **mAP50(\%)** & **Size(MB)** & **FPS** & **Macro\_P** & **Macro\_R** & **Macro\_F1** \\ \hline SSD & 76.3 & 73.2 & 70.3 & 69.5 & 97.5 & 17.1 & 75.7 & 74.6 & 69.6 \\ YOLO\_MobileNetV3 & 86.1 & 87.5 & 78.9 & 80.2 & 40.1 & 18.3 & 86.3 & 87.3 & 79.1 \\ YOLOv5 & 91.6 & 91.9 & 79.3 & 86.2 & 14.0 & 20.4 & 91.5 & 91.2 & 78.8 \\ Faster-RCNN & 84.7 & 87.3 & 73.9 & 85.7 & 110.8 & 10.6 & 84.9 & 86.7 & 74.2 \\ YOLOv6 & 92.3 & 93.1 & 83.5 & 86.0 & 14.3 & 24.1 & 92.1 & 93.0 & 83.4 \\ YOLOv7-tiny & 93.9 & 94.5 & 87.9 & 87.0 & 15.8 & 24.2 & 93.5 & 94.1 & 86.8 \\ YOLOv8 & 94.5 & 96.3 & 88.3 & 87.5 & 14.5 & 23.5 & 94.7 & 97.1 & 86.4 \\ Ours & **97.9** & **98.7** & **89.8** & **87.3** & **7.8** & **26.7** & **98.2** & **98.6** & 87.1 \\ \hline \hline \end{tabular} Bold means the best in this table.  
\end{table} 
Table 3: Detection of sewage by different detectors. It possesses 8.1 GFlops, an mAP of 89.8%, which is a 3.5% improvement over the original YOLOv5, F1 of 87.3%, a 1.1% improvement over the original model, and an FPS of 26.7, indicating a faster recognition efficiency compared to the original model. Macro_P of 98.2%, Macro_R of 98.6%, Macro_F1 of 87.1% are best compared with original YOLOv5 and other models.The model performs poorly in terms of P and recall R, yet achieves a relatively good mAP. This discrepancy may stem from the model's inadequate balance between precision and recall, but it is still able to capture a significant number of targets across various confidence levels and IoU thresholds, particularly showing better performance under more lenient thresholds. In this context, mAP provides a broader view of the model's performance, making it a more informative metric than simply relying on precision or recall alone. This section validates the effectiveness of this study. Specific results are detailed in Table 4.  
In the ablation experiments, incorporating the C3 Ghost module resulted in a significant improvement in model size, parameters, and FPS. The introduction of BiFPN enhanced the mAP, while adding the CBAM attention mechanism noticeably improved P compared to models without it. Furthermore, Grad-CAM technology was employed to generate heatmaps, which served as visual representations for the detection effects of various optimization models, as shown in Figure 16. In these heatmaps, the increase in color intensity indicates that the model pays more attention to the relevant regions. As shown in Figure 16, the heatmap generated by the model with the optimization strategy shows a more obvious focus on the target area. It is worth noting that the YOLOv5-BiFC model, which combines all optimization methods, maximizes its attention on the sewage and sewage outlet features. This further confirms that the overall detection performance of the optimized model has been improved.