Crack detection in subway tunnels based on multi-feature analysis

In the process of crack detection in subway tunnels, it is difficult to detect tunnel cracks due to the complexity of tunnel environments and the limitation of light conditions. To this effect, a tunnel crack detection method based on multi-feature analysis was proposed. Firstly, the quality of the tunnel crack image was improved by the preprocessing algorithm combining Retinex smoothing and piecewise linear stretching, and then the image was preliminarily segmented by Otsu threshold algorithm for block processing. Secondly, the area and rectangularity of connected domain in the image were analyzed, the linear structural features in the image were extracted by probabilistic Hough transform, and the pseudo crack interference was filtered out by image skeleton feature extraction algorithm. Finally, real crack detection was realized, and the detection rate of traditional crack image and tunnel crack image reached 92% and 86%, respectively. It is experimentally verified that the proposed method is practical and effective.