Pothole detection using image surveillance system: A review
1 Department of Computer Sciences, Benue State Polytechnic, Ugbokolo, Benue State, Nigeria.
2 Department of Computer Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
Review
World Journal of Advanced Engineering Technology and Sciences, 2023, 09(02), 214–222.
Article DOI: 10.30574/wjaets.2023.9.2.0210
Publication history:
Received on 08 June 2023; revised on 22 July 2023; accepted on 25 July 2023
Abstract:
Recent studies have shown that researchers have proposed various techniques for Pothole detection using data collected from different parts of the world. Automating pothole detection will go a long way in providing safe driving for road users and intelligent transportation systems. This is not only necessary to guarantee safe and adequate performance, but also to adjust to the drivers’ needs, potentiate their acceptability, and ultimately meet drivers’ preferences in bad roads. Machine learning and Object detection algorithms are mainly traditional or deep learning based. Currently, algorithms based on deep learning are widely used in various fields as a mainstream method of object detection. This paper reviewed the various pothole detection systems with different road characteristics and dataset locations. This work was able to highlight various machine learning and object detection techniques that can be applied to pothole detection which has been used in different road characteristics and their corresponding form of dataset as presented by various researchers across the world.
Keywords:
Machine Learning; Localized dataset; Object detection; Potholes; Surveillance.
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0