A Review on Negative Road Anomaly Detection Methods

IEEE Access

卷:8

Gareth Howells¹  Konstantinos Sirlantzis¹  Jihad Dib¹ 

[1] School of Engineering and Digital Arts, Jennison Building, University of Kent, Canterbury, U.K.;

关键词: Convolutional neural networks;    computer vision;    crack detection;    deep learning;    image processing;    image classification;   

DOI  :  10.1109/ACCESS.2020.2982220

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown