A crack detection system of subway tunnel based on image processing

For the images of crack defects of subway tunnel, traditional image processing algorithms is hardly effective for dealing with problems existing in the image like uneven illumination or severe noise interference. Based on pixel-level processing, an improved crack detection algorithm is proposed using structural analysis for improving the quality of tunnel images. Firstly, image preprocessing transforms the raw images of tunnel surface into binary images containing crack pixels and noise pixels. To extract crack information from binary images, three kinds of interference components are removed by structural analysis. With few interference components remaining in the image, the width of crack can be calculated according to the mean and standard deviation of the local area of the crack. Based on the algorithm, a crack detection system is designed, and a tunnel inspection experiment is conducted in a subway tunnel to capture tunnel surface images. Compared with popular image processing method, the crack recognition rate of the proposed method is 91.15% which is approximately 10% higher than others, and the measurement result of crack width based on the proposed method is closer to the ground truth. The experiment result indicates that the proposed method shows a better performance in crack detection.