An efficient and reliable approach based on adaptive threshold for road defect detection. (26th November 2021)

• Record Type: Journal Article
• Authors: Jiang, Xiaoliang; Yang, Xiaojun; Ding, Xiaokang
• Is Part Of: International journal of innovative computing and applications. Volume 12:Number 5/6(2021)
• Page Start: 321
• Page End: 329
• Publication Date: 2021-11-26
• Subjects: road crack detection -- image segmentation -- Otsu -- adaptive threshold; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.