To further enhance the accuracy of crack detection in concrete bridges,as well as to improve detection efficiency,this study proposes the YOLOv5_CBCA algorithm,based on the YOLOv5 algorithm within the one-stage object detection framework and an attention mechanism module.By integrating the convolutional block attention module(CBAM)into the CBS(Convolution,Batch Normalization,SiLU)module,the impact of downsampling on feature extraction is reduced.Additionally,the inclusion of the CA(coordinate attention)module at the tail end of the backbone network diminishes the effect of the image background,thereby increasing the precision of target localization.The effectiveness of the improved modules within the YOLOv5_CBCA algorithm is validated through ablation studies and comparative experiments.The application of this algorithm to crack images from concrete bridges,such as the Shuanglongbao and Zhongjia bridges,demonstrates its higher accuracy and better anti-interference capability,showcasing its superiority in concrete bridge crack detection.This provides a reference for the application of one-stage object detection algorithms in the identification of concrete bridge cracks.
维普
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