Crack Detection in Pavement Images Based on a Self- Adaptive Niche Algorithm

Peng Bai; Linfeng Chen; Songrong Jiang; Yu Gong; Qishen Li

doi:10.6180/jase.202206_25(3).0018

Crack Detection in Pavement Images Based on a Self- Adaptive Niche Algorithm

Peng Bai This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Linfeng Chen¹, Songrong Jiang², Yu Gong¹, and Qishen Li¹

¹Civil Aviation University of China, Air Traffic Management College, Tianjian 300300
²Hangda heavy industry (Tianjin) Co., Ltd., Tianjin 3003000

Received: October 14, 2020
Accepted: September 15, 2021
Publication Date: October 25, 2021

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202206_25(3).0018

ABSTRACT

This study focuses on optical image pavement damage detection instead of artificial detection in pavement maintenance. Based on the characteristics of cracks and combined with the niche theory, it proposes a dynamic adaptive curve extraction algorithm. First, we construct the matrix space, map the original pavement image data to the target space, process the data in target space using the multi-trough algorithm, then connect the extreme gray value points between two adjacent scanning rows with lines, compare the average gray value of the line with the average gray value of this area, and judge the possibility of cracks according to curve extension characteristics. The method considers oil, water stain, irregular concave spot, and other kinds of image noise on the pavement surface. It has good adaptability, and experimental results show that the algorithm is effective.

Keywords: pavement, crack detection, image processing, niche thought

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