Tunnel Crack Detection and Evaluation Based on Improved Holistically Nested Edge Detection Network

Crack detection is essential for the routine inspection of subway tunnels. This study addresses key challenges, such as low image acquisition efficiency, poor recognition performance, and the inability to quantify and evaluate cracks. A novel method is proposed for acquiring and detecting subway tunnel crack images, with additional features for parameter calculation and risk assessment. The approach combines deep learning with digital image processing in a tunnel crack detection system, using the multiscale block (MSBlock) module for hollow convolution and an improved holistically nested edge detection (HED) network with a feature pyramid to achieve automatic crack extraction. Postprocessing algorithms, including adaptive thresholding, connected component filtering, and morphological closing, identify crack types and compute parameters like measurements and risk scores, enhancing risk assessment. Based on the acquisition of real metro tunnel images and experimental data analysis, the results show that the proposed algorithm achieves pixel-level detection with a mean IoU of 0.8922 and an accuracy of 92.77%. The algorithm also demonstrates precision rates of 95.06% for crack length, 91.23% for maximum width, and 97.36% for pixel area.