XU Guozheng, LIAO Chencong, CHEN Jinjian, DONG Bin, ZHOU Yue
Existing detection methods for apparent crack of concrete structure are inaccurate and low-precision,losing much detail information.To address the problem,this paper proposes an apparent crack detection method for concrete based on the HU-ResNet Convolutional Neural Networks(CNN).Based on improved U-Net,the HU-ResNet model is established using the ResNet34 residual network trained by ImageNet as the encoder to retain crack details and accelerate network convergence.The scSE attention mechanism module is also introduced to recalibrate the output characteristics of the encoding block and decoding block in space and channel.At the same time,the output feature maps of each stage of the decoder are fused by the hypercolumn module to obtain more accurate semantic information and location of crack images,and the precision of which is further improved by using the combined loss function.Experimental results show that the pixel accuracy,Intersection-over-Union and F1 value of the proposed model reach 0.990 4,0.693 3 and 0.816 6 respectively,which are better than that of Canny, region growing and other traditional digital image models and FCN8s,U-Net,U-ResNet and other deep learning models,and the proposed model has more accurate crack detection results.