Detection of road cracks using Convolutional Neural Networks and Threshold Segmentation

Ashraf, Arselan and Sophian, Ali and Shafie, Amir Akramin and Gunawan, Teddy Surya and Ismail, Norfarah Nadia and Bawono, Ali Aryo (2022) Detection of road cracks using Convolutional Neural Networks and Threshold Segmentation. Journal of Integrated and Advanced Engineering, 2 (2). pp. 123-134. ISSN 2774-602X E-ISSN 2774-6038

PDF (Journal) - Published Version Restricted to Registered users only Download (554kB) | Request a copy

Abstract

Automatic road crack detection is a vital transportation maintenance responsibility for ensuring driving comfort and safety. However, manual inspection is considered risky because it is time-consuming, costly, and dangerous for inspectors. Automated road crack detecting techniques have been extensively researched and developed in order to overcome this issue. Despite the difficulties, most proposed methodologies and solutions involve machine vision and machine learning, which have recently acquired traction largely due to the increasingly more affordable processing power. Nonetheless, it remains a difficult task due to the inhomogeneity of crack intensity and the intricacy of the background. In this paper, a convolutional neural network-based method for crack detection is proposed. Recent advancements inspire the method of machine learning to computer vision. The primary goal of this work is to employ convolutional neural networks to detect road cracks. Data in the form of images has been used as input, preprocessing, and threshold segmentation are applied to the input data. The processed output is fed to CNN for feature extraction and classification. The training accuracy was found to be 96.20 %, the validation accuracy to be 96.50 %, and the testing accuracy to be 94.5 %.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Crack Detection, Computer Vision, Convolutional Neural Networks, Machine Learning.
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Ali Sophian
Date Deposited:	03 Jan 2023 08:36
Last Modified:	03 Jan 2023 08:36
URI:	http://irep.iium.edu.my/id/eprint/102443

Actions (login required)

View Item