Abstract: In this paper, a bridge disease detection system based on YOLOv5 algorithm is proposed for UAV bridge disease detection. Firstly, diseases are collected through UAV cruise system. Secondly, K-means clustering algorithm is applied to improve the prior box proportion in YOLOv5 algorithm to obtain the size of bridge disease. Thirdly, the UAV input image is cut to 640 * 640 size by using the image cutting algorithm to reduce the difficulty of model training and reasoning. What's more, this paper uses APAP algorithm to splice the recognition results of cutting images to achieve a complete UAV image recognition result. Finally, the paper employs Python Flask to build an open bridge disease detection Web front-end, which can call the disease weights of different bridge parts according to actual needs, and further realize real-time detection of 13 bridge diseases. The results show that the model can effectively reduce the hardware requirements, directly identify and process UAV images, and achieve rapid disease detection, which is expected to provide an efficient, safe and promising detection method for bridge disease detection.