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A Review on Negative Road Anomaly Detection Methods

Dib, J., Sirlantzis, K., Howells, G. (2020) A Review on Negative Road Anomaly Detection Methods. IEEE Access, . ISSN 2169-3536. (doi:10.1109/ACCESS.2020.2982220) (KAR id:80607)


The main limitation to obstacle avoidance nowadays has been negative road anomalies which is the term we used to refer to potholes and cracks due to their negative drop from the surface of the road. This has for long been a limitation because of the fact that they exist in different, random and stochastic shapes. Today’s technology lacks the presence of sensors capable of detecting negative road anomalies efficiently as the latter surpasses the sensor’s limitations rendering the sensing technique inaccurate. A significant amount of research has been focused on the detection of negative road anomalies due to the fact that this topic is becoming a hot research topic. In this paper, the existing techniques will be reviewed. Their limitations will be highlighted and they will be assessed via certain performance indicators and via some chosen criteria which will be introduced.

Item Type: Article
DOI/Identification number: 10.1109/ACCESS.2020.2982220
Projects: AI based socially assistive robotics
Uncontrolled keywords: Convolutional neural networks, Computer vision, Crack detection, Deep learning, Image processing, Image classification, Image texture analysis, Machine learning algorithm, Multi-layer neural networks, Negative road anomalies detection, Pothole detection, Real-time.
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.P3 Pattern recognition systems
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: 26 Mar 2020 13:10 UTC
Last Modified: 04 Mar 2024 15:23 UTC
Resource URI: (The current URI for this page, for reference purposes)

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