## 4 Results and Analysis
### Evaluation Metrics Results and Discussion
The same importance as the detection speed is the reliable performance  
Figure 8: The PR curve plot of the relevant experimental models in this paper was tested in the BDD test set. (**a**) PR curve of all classes, (**b**) PR curve of the cracks (**c**) PR curve of the efflorescence, and (**d**) PR curve of the rust staining. of the model. In Table 2, we analyze the performance of the four types of damage models in mAP@.5, Precision, Recall, and F1-Score evaluation metrics. The purpose is to evaluate the model proposed in this paper in a comprehensive and detailed manner. In this paper, mAP@.5 not only shows the accuracy of model classification but also evaluates its target localization ability, which is an important evaluation index in this work. The mAP@.5 of YOLOv3-Tiny is only 0.375, the mAP@.5 of YOLOv5S is 0.807, and the mAP@.5 of B-YOLOv5 SY improved by enhanced feature extraction is 0.811, an increase of 0.4% compared with the unimproved. The enhanced feature extraction of the optimized sample imbalance processing function BE-YOLOv5S network model mAP@.5 is 0.827, which is 45.1% higher than YOLOv3-Tiny, 2% higher than YOLOv5S, and 1.6% higher than B-YOLOv5S. It can be seen that using the BiFPN network structure to enhance the feature extraction is more suitable for the features of the bridge damage target. With the optimization of the EFL sample unbalance function, the bridge damage detection BE-YOLOv5S model proposed in this paper achieves a significant improvement in mAP@.5 evaluation metrics. In the Precision evaluation metrics, YOLOv3-Tiny, YOLOv5S, B-YOLOv5S, and BE-YOLOv5S obtained scores of 0.293, 0.867, 0.841, 0.893, respectively. The BE-YOLOv5S proposed in this paper has the highest score. Compared with the first three models, the improvement was 57.4%, 2.6%, and 5.2% respectively. In the Recall evaluation indicators, YOLOv3-Tiny, YOLOv5S, B-YOLOv5S, and BE-YOLOv5S obtained scores of 0.701, 0.817, 0.803, and 0.821, respectively. The BE-YOLOv5S proposed in this paper has the highest score; compared with the highest score; compared with the previous three models, it obtained 44.2%, 1.4%, and 3.3% improvement. Through the evaluation of the above evaluation indicators, the BE-YOLOv5S bridge damage detection model proposed in this paper has shown good reliability and advancement. We strengthen the feature extraction capability of the bridge damage detection model BE-YOLOv5S in bridge damage targets by introducing the BiFPN network structure--it significantly improves the detection of small and indistinct features such as cracks, and improves the engineering applicability of the bridge damage detection model. At the same time, to establish a model with better performance, we improved Focal Loss and used EFL to improve the sample imbalance processing mechanism for the narrow and complex characteristics of bridge damage images. The experimental results show that, by improving the handling method of sample imbalance, the BE-YOLOv5S proposed in this paper can efficiently acquire image information in the process of training and learning. The optimization of sample imbalance processing plays an important role in the establishment of a practical bridge damage detection model. Through the comparative analysis of evaluation indicators, our established BE-YOLOv5S network model has advanced detection capabilities in the detection of bridge damage. Compared with the representative YOLOv3-Tiny and YOLOv5S networks in the field of lightweight target detection, they have achieved significant improvements in various evaluation indicators.