Research on an image-based crack detection method for subway tunnels based on feature analysis

To effectively detect the surface cracks of subway tunnels, an automatic tunnel crack detection system based on machine vision is presented. Aiming at the problems of environmental complexity and low contrast in subway tunnels, the image texture feature is first enhanced by the methods of frequency domain filtering and spatial differencing. Then, depending on the characteristics of the tunnel cracks in question, the crack propagation method is used to extract the complete cracks. Finally, broken cracks are connected during processing, and the method of combining projection and threshold is used to determine the crack types. At the same time, characteristics such as the length, width, and area of the cracks are obtained. The experimental results show that the presented methods can effectively extract complete cracks in complex tunnel environments. The identification error of tunnel crack parameters meets the actual engineering requirements.