Crack detection algorithm based on YOLOv4 optimization for concrete buildings

To solve the problem of irregular crack trend and difficult to extract the characteristics of small cracks in concrete buildings, an improved crack detection algorithm based on YOLOv4 was proposed. Based on YOLOv4 framework, RFB module with wider receptive field is introduced in the feature extraction network to capture feature images. Based on the multi-scale path fusion structure of PANet, a new multi-scale feature fusion method sl-PANET is proposed. Firstly, the shallow network feature information is added to improve the accuracy of the model in identifying fine cracks. Secondly, the upper sampling module of DUpsampling is adopted to fully restore the image feature information. The CBAM attentional mechanism module was incorporated in the up-sampling and down-sampling processes to highlight the fracture feature information and remove the interference of background redundant information, so as to enhance the expression ability of fracture feature. The AdamW optimizer is also used to accelerate the convergence of network training. Experimental results show that the detection accuracy of the improved algorithm is as high as 94.47%, which is 6.44% higher than the original YOLOv4 algorithm, and can meet the current crack detection requirements of concrete buildings.