## 5 Conclusions  
In the work in this paper, we propose a lightweight convolutional neural network model BE-YOLOv5S with enhanced feature extraction and an improved sample imbalance processing mechanism, we demonstrate the advances and reliability of our work in bridge damage detection by experimental analysis. First, we carried out the re-selection and labeling of the dataset established by the bridge damage detection model based on the public dataset, which reduced the error rate of mislabeling, At the same time, we obtained the data used in the experiments of this paper using the manual collection and web crawler and established the BDD dataset. To further test the generalization ability and reliability performance of the model in this experiment, we enhanced the dataset based on the BDD dataset. To a certain extent, the size and position of the original bridge damage targets are changed, and the brightness and clarity are processed to simulate the complex environmental conditions in actual engineering. We noticed that there are very few public datasets applicable to the YOLO algorithm. To better contribute our work to the field of bridge damage detection, we decided to disclose the BDD dataset and the enhanced BDD-E dataset we used. Through the disclosure of data, we aim to contribute to our work and facilitate scientific research for more researchers who are engaged in the research of the YOLO algorithm of lightweight convolutional neural networks. After the dataset is established, we introduce the representative YOLOv3-Tiny model in the field of lightweight target detection, the advanced YOLOv5S, and the B-YOLOv5S network model with enhanced feature extraction for experimental comparison and analysis. Under the same experimental environment, the BE-YOLOv5S model proposed in this paper has achieved significant advantages in various indicators and testing under complex conditions, especially in the field of target detection, which is an important indicator. In mAP@.5, the damage detection model on the BE-YOLOv5S bridge leads the YOLOv3-Tiny, YOLOv5S and B-YOLOv5S network models by 45.1%, 2%, and 1.6%, respectively, with a certain degree of advancement. Similarly, in the important index of F1-Score, the damage detection model on the BE-YOLOv5S bridge is ahead of the YOLOv3-Tiny, YOLOv5S, and B-YOLOv5S network models by 44.2%, 1.4%, 3.3%, respectively, and is more reliable in bridge damage detection. BE-YOLOv5S shows significant advantages in detection under complex conditions, and the BE-YOLOv5S bridge damage detection model achieves significant advantages in the detection of illumination, sharpness, and multiple small targets. Through the experiments and results analysis in this paper, we find that the BE-YOLOv5S network model proposed in this paper has good robustness and advanced reliability in the detection of bridge damage targets. The bridge damage detection model BE-YOLOv5S proposed in this paper has a practical engineering application type, good flexibility, and reliability, and can be suitable for the detection of embedded devices in complex environments where bridges are located. Compared with the current mainstream lightweight detection models YOLOv3-Tiny and YOLOv5S, the BE-YOLOv5S proposed in this paper is more advanced and applicable. However, for the deep learning model of bridge damage detection, the large-scale and high-quality data is the basis for the strong generalization ability of the model [63], Therefore, in the next step, we will further improve the quality and scale of the data and improve the BE-YOLOv5S model, and improve the work of the lightweight convolutional neural network in bridge damage detection. Conceptualization, F.D. and S.J. ; methodology, software, validation, formal analysis, investigation, F.D. ; resources, F.D. ; data curation, writing--original draft preparation, F.D. and K.C. ; writing--review and editing F.D. and K.C. ; visualization, F.D. ; supervision, S.J. ; project administration, F.D. All authors have read and agreed to the published version of the manuscript.