Application of CEM Algorithm in the Field of Tunnel Crack Identification

Abstract

Cracks are one of the most common and serious diseases of tunnel lining, which seriously threatens the safety of vehicles and requires regular inspection and measurement. In view of the problems of underexposure, uneven illumination and serious noise of the collected images in the tunnel, after the image is evenly processed, a denoising method combined with median filtering and bilateral filtering is constructed, which can filter out a lot of noise on the basis of protecting the details of the crack edge. Due to the large number of mechanical scratches and disturbing textures in the tunnel lining, EMAP is used to enhance features after Gabor filtering, and the improved CEM segmentation algorithm is used to effectively overcome the inaccurate segmentation of traditional algorithms and obtain binary images of cracks. The experimental results show that the proposed algorithm can identify the accuracy of tunnel lining cracks by more than 92%, which verifies the effectiveness of the proposed algorithm.