Automatic subway tunnel crack detection system based on line scan camera

Abstract

Automatic visual inspection of cracks on concrete surface has become increasingly important for the health diagnosis of subway tunnel structures to prevent catastrophic failures. This paper designs an on-board image acquisition system using multiple line scan cameras to capture the full-section images of tunnel surface and proposes a robust automatic crack detection method for noisy and complicated tunnel surface images. The detection method consists of three steps. The first step is a crack enhancing process by using morphological image processing and frequency-domain enhancing algorithm. In the second step, a multistage fusion filtering algorithm is designed to filter out background noise and interference from images of water stain and various railway devices on the tunnel surface. The third step is to use an improved seed growth algorithm to segment real cracks from the noisy background. The detection system is tested in the Beijing Metro Line 1, and the results demonstrate that the proposed method outperforms existing methods that are only robust for images of small area with a simple background.