## 4 Results and Analysis
### Evaluation Metrics Results and Discussion  
The evaluation of the model is very important for the verification of the model performance. To comprehensively and quantitatively analyze the performance of the bridge damage detection model established in this paper, we draw the relevant data display diagrams according to the performance of the four types of models in the BDD test set. In Figure 7, according to the basic indicators of the four types of confusion matrices, we draw the confusion matrices of the relevant experimental models in this paper tested in the BDD test set. As shown in Figure 7, the performance of the YOLOv3-Tiny model in the classification of three types of bridge damage is poor, far lower than the other three types of bridge damage detection models; YOLOv5S, B-YOLOv5S, BE-YOLOv5S. The three models have the same ability to classify efflorescence, YOLOv5S is more sensitive to the  
Figure 6: Plots of training and loss functions for four types of models during the experiment. (**a**) Diagram of Train Box Loss function; (**b**) Diagram of Train Classes Loss function; (**c**) Diagram of Train Object Loss function; (**d**) Diagram of Value Box Loss function; (**e**) Diagram of Value Classes Loss function; (**f**) Diagram of Value Object Loss function. features of rust staining, and the B-YOLOv5S model with enhanced feature extraction greatly reduces the false detection rate of rust staining in background detection. Through feature enhancement, deep-level information can be mined, which is beneficial for the model to distinguish the background; the BE-YOLOv5S model after sample imbalance optimization is more sensitive to small target cracks. To more intuitively discover the specific performance of each model in the bridge damage classification prediction, we draw the PR curves of the relevant experimental models in this article, as shown in Figure 8.  
As shown in Figure 8a, the PR curve of BE-YOLOv5S almost wraps the other three types of curves, which directly proves that the performance of BE-YOLOv5S in bridge damage detection is ahead of the other three types of models. For the PR curve analysis of cracks, efflorescence, and rust staining, it is intuitively found that the performance of the YOLOv3-Tiny lightweight detection network model in bridge damage detection is poor. By drawing the balance point F1 (the value when P = R, that is, when the slope is 1), we can  
Figure 7: The confusion matrix plot of the relevant experimental models in this paper was tested on the BDD test set. (**a**) YOLOv3-Tiny; (**b**) YOLOv5S; (**c**) B-YOLOv5S; (**d**) BE-YOLOv5S. find that the BE-YOLOv5S bridge damage detection model proposed in this paper is ahead of other detection models in all kinds of damage detection performance in the experiment. To evaluate the work in this paper in more detail, we tested the detection speed and mAP@.5, Precision, Recall, and F1-Score indicators of various models through experiments, as shown in Table 2. Given the special environment of bridge structures, there is a high demand for the detection speed of the model (for example, using UAVs to carry embedded devices for detection); a slow detection speed not only results in the loss of time and cost but also reduces the detection coverage, resulting in seriously missed detection. The detection speed of the four types of damage detection models for bridge damage is shown in Table 2. The YOLOv3-Tiny model has the fastest detection speed, and the BE-YOLOV5 and YOLOv5S models have similar detection speeds. The B-YOLOv5S model is improved by the BiFPN network structure However, the BE-YOLOv5S network model after the sample imbalance treatment has significantly improved the detection speed of bridge damage. It can be seen that through the optimization of EFL sample imbalance, the established model can be more suitable for the feature learning of bridge damage, thereby improving the detection speed of the model.