Bridge Crack Recognition Method Based on Yolov5 Neural Network Fused With Attention Mechanism

To solve the problem of low detection accuracy of deep learning targets for crack detection in complex backgrounds, The objective of this study is to propose a fusion (CBAM) attention mechanism to enhance the YOLOv5 model for high-precision bridge crack recognition. At the same time, a consistent bridge crack dataset covering different lighting conditions and humidity environments is constructed. To validate the effectiveness of the proposed model, it was compared and analyzed with current mainstream deep learning models (e.g., CNN, VGG19,etc.).The experimental results indicate that incorporating the CBAM attention mechanism, the proposed model was improved by 5.6% in precision, 1.2% in recall, and 13% in optimization of the integrated discrimination parameters compared to the original YOLOv5 model. In addition, in terms of width detection. The experimental results indicate that the model can reach 0.01 mm in measurement accuracy, The research contributes to the efficiency, precision and intelligence of concrete bridge crack detection.