Road Crack Detection Using Adaptive Multi Resolution Thresholding Techniques

Othman, Zuraini and Abdullah, Azizi and Kasmin, Fauziah and Syed Ahmad, Sharifah Sakinah (2019) Road Crack Detection Using Adaptive Multi Resolution Thresholding Techniques. Telkomnika (Telecommunication Computing Electronics and Control), 17 (4). pp. 1874-1881. ISSN 1693-6930

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Abstract

Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist's knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5 × 5 partitioned image outperforming the other partitioned scales.

Item Type:	Article
Uncontrolled Keywords:	Edge detection, Fixed partitioning, Machine vision, Multi-resolution level, Road crack detection
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	08 Dec 2020 12:47
Last Modified:	08 Dec 2020 12:47
URI:	http://eprints.utem.edu.my/id/eprint/24575
Statistic Details:	View Download Statistic

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