Development of an Intelligent Road Condition Monitoring System using Citizen Sensing Technique

Road transportation is Nigeria’s most important mode of transportation, due to increase in vehicle ownership and the role of roads in economic activities. Road anomalies, which include potholes and speed bumps, impede the movement of traffic on roads. Therefore, there is a need for an intelligent system to detect these road anomalies and document them. Besides providing real-time assistance to road users and future driverless vehicles, an intelligent road information system can be used to build databases of road conditions across the country and to create road remediation strategies and schemes for costing road repairs. This paper presents the development of an intelligent, end-to-end road condition monitoring system using a citizen sensing approach.  The approach was employed because it allows road users to gather road data with dedicated devices rather than installing sensors along the roadway. The system detects potholes using a deep learning-based object detection model (Tiny YOLOv4). The dataset used in validating the approach consisted of 1,265 images for training, 401 images for testing and 118 images for validation. The model had an overall precision of 77.00%, recall of 66.00%, F1-score of 71.00%, average IoU of 58.65% and mean average precision (mAP@0.50) of 69.99%. Based on the obtained results, the system demonstrated the ability to detect potholes on the road surface. Furthermore, this system establishes a critical first step towards a large-scale, cost-effective remote road monitoring infrastructure. Additionally, the use of citizen data collected by road users significantly reduces the cost of deploying the technique.