Detection of Potholes Using Image Processing Method

Muhammad Zulkifli, Abdullah Norhairi and Norazlianie, Sazali (2024) Detection of Potholes Using Image Processing Method. In: Intelligent Manufacturing and Mechatronics, Lecture Notes in Networks and Systems. 4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023 , 07 – 08 August 2023 , Pekan, Malaysia. pp. 597-606., 850. ISSN 2367-3389 ISBN 978-981-99-8819-8

Preview	Pdf Detection of Potholes Using Image Processing.pdf Download (49kB) | Preview
	Pdf Detection of Potholes Using Image Processing Method.pdf Restricted to Repository staff only Download (399kB) | Request a copy

Abstract

Potholes are a common problem on roads, caused by weather, vehicle activity, and poor maintenance. Potholes can be hazardous for drivers, cars, and motorcycle riders. Potholes are often filled with asphalt or concrete. A methodology for automatically identifying potholes on road surfaces using computer vision methods is potholes detection utilizing image processing. This technique can be used to improve road maintenance by quickly locating potholes, enabling early repairs, and lowering the risk to drivers and their cars. This study emphasizes a Gaussian noise filtering technique for the developed infrastructure of image pre-processing stage. Thus, this study also suggests four methods for segmentation detecting potholes in images: image thresholding (Otsu), Canny edge detection, K-means clustering, and fuzzy C-means clustering. The effectiveness of the different image segmentation techniques was tested in MATLAB 2019a, and the results were generated in terms of accuracy and precision. The results were compared with each other to draw a conclusion on their viability.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Image processing; Pothole detection; Fuzzy C-means clustering; Canny edge detection; Image thresholding; K-means clustering
Subjects:	T Technology > TS Manufactures
Faculty/Division:	Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Miss Amelia Binti Hasan
Date Deposited:	08 May 2024 07:48
Last Modified:	16 May 2024 04:26
URI:	http://umpir.ump.edu.my/id/eprint/41142
Download Statistic:	View Download Statistics

Actions (login required)

View Item