Please use this identifier to cite or link to this item:
Title: Survey of road anomalies detection methods
Authors: Saffarini, Rasha$AAUP$Palestinian
Khamayseh, Faisal$Other$Palestinian
Awwad Daraghmi, Yousef$Other$Palestinian
Elyan, Derar$Other$Palestinian
Sabha, Muath$AAUP$Palestinian
Keywords: anomaly detection
Image processing
Computer Vision
Issue Date: 29-Sep-2023
Publisher: Inder Science
Citation: International Journal of Intelligent Systems Technologies and Applications
Series/Report no.: 1740-8873;280-302
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 recognizing road anomalies. The related studies can be categorized into accelerometer-based techniques and vision-based techniques. In both techniques, deep learning and mathematical methods have been utilized. 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.
Appears in Collections:Faculty & Staff Scientific Research publications

Files in This Item:
File Description SizeFormat 
Survey_of_Road_Anomalies_Detection_Methods-2022-10-30-07-32.pdf2.04 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Admin Tools