Pavement Crack Detection in Infrared Images Using a DCNN and CCL Algorithm

In-pavement crack detection, image segmentation techniques can accurately identify and extract cracks in pavement images with complex backgrounds, however, due to the interference of noise and non-crack damage, the resulting irrelevant non-crack regions reduce the extraction accuracy. To address the above problems, this research presents a road crack segmentation model consisting of a skip connection and decoder of U-Net, residual blocks in the encoding part, and the connected components labeling algorithm (CCL) in the extremity. The proposed method, depth-wise separable convolution replaces the standard convolution to reduce the number of parameters and operation cost. Post-processing using the connected components labeling algorithm solves the interference of irrelevant non-cracked regions. 4710 infrared images are used for the training and testing of the model. The testing results show that the new method for the pavement crack detection in different shadowed images is satisfactory, the detection accuracy can be up to 92.23%, and the algorithm comparison proves that the proposed algorithm is much better than that by the widely used traditional algorithms. In terms of similarity, the Dice Similarity Coefficient (DSC) is improved by 5.97%, 5.22%, and 1.49% compared to FCN, U-Net, and DeeplabV3+ networks, respectively. Therefore, the method is effective in detecting cracks in different pavement IR images, and can significantly improve the accuracy of segmentation results, reduce the error rate, improve the segmentation accuracy and robustness, and can extract cracks in pavement IR images well.