Title: Survey of road anomalies detection methods

Authors: Rasha Saffarini; Faisal Khamayseh; Yousef-Awwad Daraghmi; Derar Elyan; Muath Sabha

Addresses: Department of Natural, Engineering and Technology Sciences, Arab American University, Jenin, Palestine ' Department of Computer Systems Engineering, Palestine Polytechnic University, Hebron, Palestine ' Department of Computer Systems Engineering, Palestine Technical University, Tulkarm, Palestine ' Department of Applied Computing, Palestine Technical University, Tulkarm, Palestine ' Department of Multimedia Technology, Arab American University, Jenin, Palestine

Abstract: Automatic road anomaly detection and recognition systems are essential due to their effect on the comfort and safety of drivers and passengers. Drivers should be aware of bad road conditions and the existence of anomalies in routes to avoid accidents, reduce the possibility of car malfunction, and take the most appropriate route to their destinations. This led to increased research interest in automatically detecting and recognising road anomalies. The related studies can be categorised into accelerometer-based techniques and vision-based techniques. In both techniques, deep learning and mathematical methods have been utilised. This paper reviews the latest research in the anomaly detection and classification field. Several types of road anomalies are discussed, such as potholes, cracks, and speed bumps. Additionally, road damage detection techniques are used for different types of road anomalies, challenges, and limitations of current research.

Keywords: road anomalies; computer vision; deep learning; image processing.

DOI: 10.1504/IJISTA.2023.133700

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.3, pp.280 - 302

Received: 30 Jan 2023
Accepted: 08 May 2023
Published online: 29 Sep 2023 *

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