A Review of the Different Proposed Image Detection Techniques for Road Anomalies Detection

Abstract

Road anomalies such as, potholes, speed bumps and cracks have been one of the major problems faced by road users globally, contributing to vehicular accidents on roads, quick wear and tear of vehicles, among others. Overtime, advancement in vehicular technology and sensors, has led to the establishment of automated approach for detecting road anomalies. Though, these approaches can be categorized into 3D reconstruction-based approach, 2D vision-based approach and the vibrational-based approach. These techniques are geared towards enabling vehicles in detecting road anomalies and notifying drivers of such, thereby reducing the rate of accidents on roads caused by these anomalies. Also, these approaches can be incorporated into vehicular Ad-hoc network, where automated vehicles can be able to navigate through road terrains with pothole anomalies. However, the 2D vision-based approach has received wide acceptance among the academia and the industry due to its advantages over the 3D reconstruction-based, such as, lower cost of implementation. Also, it has advantage over the vibrational-based approaches in terms of better detection accuracy. In this regard, this paper presents a mini-survey of various 2D vision-based techniques proposed in the literature for road surface condition monitoring and anomaly detection. The merits and drawbacks of these techniques are highlighted. Furthermore, open research issues are presented. Our effort forms part of a larger goal aimed at improving and developing robust vision-based approaches for road anomaly detection, as well as giving researchers a pathway in similar pursuits.