Eliminating Lining Seams in Tunnel Concrete Crack Images via Line Segments’ Translation and Expansion

Abstract

Cracks are the main form of tunnel complications and have drawn much attention in current tunnel safety monitoring. However, automatic crack detection cannot accurately extract cracks from tunnel concrete lining surfaces because of the inference of noises like lining seams, which have similar gray values and textures to cracks, and increase the topological complexity of cracks. In this paper, we proposed a novel method for eliminating lining seams in tunnel concrete crack images, to address the above-mentioned issues. Our contributions are shown as follows: 1) classified arbitrary line segments to better distinguish the edges on the cracks and the seams, and proposed uniform k-divided angle model to get accurate classification and 2) introduced two new principles to help effectively eliminate lining seams through the information of translation direction and expansion width of all line segments. The experimental results proved the superior precision and efficiency of our method compared with the existing methods.

Crack Detection of Concrete Pavement With Cross-Entropy Loss Function and Improved VGG16 Network Model

“…proposed an edge-based crack detection method, which used a width map to remove noise and residual noise can be removed by reclassification of crack regions. In 2019, Qu et al [3], [4] proposed a genetic algorithm based on genetic programming (GP) and percolation model, which can detect cracks on the concrete pavement under different noises. In 2018, Su and Yang et al [5] proposed an edge detection method based on the morphological segmentation to achieve the segmentation of concrete cracks.…”