Title: An efficient and reliable approach based on adaptive threshold for road defect detection

Authors: Xiaoliang Jiang; Xiaojun Yang; Xiaokang Ding

Addresses: College of Mechanical Engineering, Quzhou University, Quzhou, 324000, China; Key Laboratory of Air-Driven Equipment Technology of Zhejiang Province, Quzhou University, Quzhou, 324000, China; College of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China ' College of Mechanical Engineering, Quzhou University, Quzhou, 324000, China; Key Laboratory of Air-Driven Equipment Technology of Zhejiang Province, Quzhou University, Quzhou, 324000, China ' College of Mechanical Engineering, Quzhou University, Quzhou, 324000, China; Key Laboratory of Air-Driven Equipment Technology of Zhejiang Province, Quzhou University, Quzhou, 324000, China

Abstract: With the rapid development of economy, automatic detection of road crack becomes a hot research study. However, it still has immense challenges due to the shading, intensity inhomogeneity and divots in the crack image. Traditional detection algorithms use the characteristic of the image such as intensity and texture to segment the crack regions, so they cannot achieve satisfactory performance. In this article, we introduce an automatically image-based method for road crack detection. Firstly, a new mask image is constructed based on the grey-mean of original image. Secondly, calculating the difference between the original image and the mask image and selecting the highest value to extract the targets from the image background. Finally, the redundant interference is removed by morphological operation. Experimental results concentrate on public datasets show that our method is more reliable than previous approaches.

Keywords: road crack detection; image segmentation; Otsu; adaptive threshold.

DOI: 10.1504/IJICA.2021.10043066

International Journal of Innovative Computing and Applications, 2021 Vol.12 No.5/6, pp.321 - 329

Received: 21 May 2020
Accepted: 23 Nov 2020

Published online: 26 Nov 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article