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Application of Deep Learning Techniques in Negative Road Anomalies Detection

Dib, Jihad, Sirlantzis, Konstantinos, Howells, Gareth (2022) Application of Deep Learning Techniques in Negative Road Anomalies Detection. In: Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 1: ROBOVIS. . pp. 475-482. ISBN 978-989-758-611-8. (doi:10.5220/0011336000003332) (KAR id:97686)

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Negative Road Anomalies (Potholes, cracks, and other road anomalies) have long posed a risk for drivers driving on the road. In this paper, we apply deep learning techniques to implement a YOLO-based (You Only Look Once) network in order to detect and identify potholes in real-time providing a fast and accurate detection and sufficient time for proper safe navigation and avoidance of potholes. This system can be used in conjunction with any existing system and can be mounted to moving platforms such as autonomous vehicles. Our results show that the system is able to reach real-time processing (29.34 frames per second) with a high level of accuracy (mAP of 82.05%) and detection accuracy of 89.75% when mounted onto an Electric-Powered Wheelchair (EPW).

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.5220/0011336000003332
Uncontrolled keywords: Pothole Detection, Road Anomaly, Deep Learning, Deep Neural Network, Convolutional Network, Image Processing, Object Detection, Object Classification.
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: Engineering and Physical Sciences Research Council (
Depositing User: Jihad Dib
Date Deposited: 29 Oct 2022 13:57 UTC
Last Modified: 31 Oct 2022 14:24 UTC
Resource URI: (The current URI for this page, for reference purposes)
Dib, Jihad:
Sirlantzis, Konstantinos:
Howells, Gareth:
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