## 4 Results and Analysis
### Result and Discussion of Testing under Complex Conditions  
In fact, the influence of FOV in actual engineering has a certain impact on the light and viewing angle of the inspection perspective. For the deep learning network model, the same object is different from different shooting angles, locations and lighting conditions. Therefore, in practical engineering applications, FOV is an important issue that needs  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model.** & **mAP@.5** & **Precision** & **Recall** & **F1-Score** & **FPS** \\ \hline YOLOv3-tiny & 0.376 & 0.293 & 0.701 & 0.413 & 244 \\ YOLOv5S & 0.807 & 0.867 & 0.817 & 0.841 & 189 \\ B-YOLOv5S & 0.811 & 0.841 & 0.803 & 0.822 & 156 \\ BV-YOLOv5S & 0.827 & 0.893 & 0.821 & 0.855 & 185 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation Results of Four Types of Models.  
to be considered. We compare the detection effects of the models on the BDD-E dataset, aiming to comprehensively evaluate the reliability and advancement of our proposed BE-YOLOv5S model for bridge damage detection. We found that the BE-YOLOv5S bridge damage detection model has excellent detection performance and is more reliable in the detection of enhanced illumination and multiple small targets. As shown in Figure 9, the test images of the four types of models in the enhanced care and multi-small target environments are shown. Only BE-YOLOv5S can be successfully detected in the detection of enhanced illumination cracks and efflorescence, which indicates that the traditional Focal Loss using the same modulation factor balance training cannot meet the training requirements of the bridge damage detection model when faced with strong illumination effects. In this paper, FEL is used to optimize the imbalance problem of the training samples of bridge damage, and the learning and training using the modulation factor associated with the damage category achieve significant performance improvement. As shown in Figure 9c,d YOLOv5S and B-YOLOv5S are not sensitive to the small object detection in the upper right corner, However, the rust staining target detection confidence of B-YOLOv5S with enhanced feature extraction is significantly higher than that of YOLOv5S without enhanced feature extraction. It can be seen that after BiFPN feature extraction, deeper target information can be excavated. Therefore, the bridge damage target is detected more effectively, but the sensitivity is not high in the detection of small targets. As shown in Figure 9e, the optimized BE-YOLOv5S bridge damage detection model through enhanced feature extraction and sample imbalance processing will detect all damage targets in the image to be detected, the sensitivity to small targets is high, and it has a more reliable performance for practical engineering applications.  
Figure 9: Comparison chart of detection performance of four types of bridge damage models under complex conditions. (**a**) Undetected original image; (**b**) Performance rendering of YOLO-V3-Tiny model detection; (**c**) rendering of YOLOv5S model detection performance; (**d**) rendering of B-YOLOv5S model detection performance; (**e**) BE-YOLOv5S model detection performance renderings (the images used are all bridge damage images from the BDD-E dataset).