Research on bridge crack identification based on improved YOLO v8

In this paper, the crack recognition function of the bridge surface crack detection system is mainly realized, and the crack identification and positioning model is constructed through the deep learning algorithm to realize the fast and high-precision identification and positioning of cracks. In view of the complex scene of bridge deck detection and the diversity of crack types, an improved recognition model based on YOLO v8 was proposed, which can quickly and accurately frame the crack location and provide convenience for subsequent work. For the lightweight of Bottleneck, the GAM attention mechanism module is introduced to improve the recognition effect. Experiments show that compared with the original YOLO v8 model, the YOLO v8-FG model has been improved in various indicators, including the accuracy increased by 4.9%, the recall rate increased by 4.7%, the mAP increased by 5.5%, the detection frame rate increased by 25.56, and the model size decreased by 19.5MB. best.