Research on target detection algorithm of cracks in concrete bridges

This paper presents a novel approach to addressing the challenges of manual on-site detection of bridge cracks, including time-consuming, labor-intensive, low safety factor, and low identification accuracy. The proposed method is based on an improved version of the YOLO v3 algorithm for bridge crack object detection. This method achieves the detection and identification of bridge cracks. The experimental results show that the improved YOLO v3 object detection algorithm achieves an accuracy of 90.26% on the test set and has good recognition performance.