Evaluating the Efficacy of Computer Vision in Predicting and Detecting Road Damage for Intelligent Transport Systems

Authors

  • Mohd Omar, Pradeep Kumar

Keywords:

Intelligent Transport System, Computer vision, Machine Learning, Deep Learning

Abstract

Improving road damage prediction and detection through state-of-the-art technology, especially computer vision, is an exciting new direction in Intelligent Transport Systems (ITS), which places a premium on highway safety. In the framework of Intelligent Transport Systems, this review article assesses the effectiveness of computer vision methods in improving road safety via the precise prediction and identification of road damage. The paper commences with a comprehensive examination of the prevailing obstacles in conventional road damage detection methods, emphasizing the imperative for inventive resolutions to circumvent constraints in present strategies. The next step is to investigate several in-depth approaches to computer vision, including everything from traditional image processing methods to cutting-edge deep learning algorithms. We focus on their merits, limits, and usefulness when predicting and detecting different types of road damage. The review summarizes the progress in computer vision for road damage detection by thoroughly analyzing pertinent literature and case studies. It highlights essential breakthroughs and identifies areas that could use additional improvement. The critical review considers the possible effect on road safety as a whole, as well as scalability concerns and difficulties with actual implementation. In addition, the paper explores the amalgamation of computer vision with other technologies, such as sensor networks and data analytics, to develop all-encompassing and resilient systems for improving road damage prediction and detection. The findings reported in this review enhance our comprehension of the present condition of computer vision within the framework of Intelligent Transportation Systems (ITS), providing valuable insights into its possible impact on defining the future of roadway safety. This study aims to consolidate information to guide academics, practitioners, and policymakers in promoting progress that would ultimately enhance the safety and efficiency of intelligent transportation systems.

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Published

24.03.2024

How to Cite

Pradeep Kumar, M. O. (2024). Evaluating the Efficacy of Computer Vision in Predicting and Detecting Road Damage for Intelligent Transport Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 2553–2562. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5727

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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