Augmenting roadway safety with machine learning and deep learning: Pothole detection and dimension estimation using in-vehicle technologies

Abstract

Detection and estimation of pothole dimensions is an essential step in road maintenance. Aging, heavy rainfall, traffic, and weak underlying layers may cause pavement potholes. Potholes can cause accidents when drivers lose control after hitting or swerving to avoid them, which may lead to injuries or fatal crashes. Also, potholes may result in property damages, such as flat tires, scrapes, dents, and leaks. Additionally, potholes are costly; for example, in the United States, potholes cost drivers about $3 Billion annually. Traditional ways of attending to potholes involve field surveys carried out by skilled personnel to determine their sizes for quantity and cost estimates. This process is expensive, prone to errors, subjectivity, unsafe, and time-consuming. Some authorities use sensor vehicles to carry out the surveys, a method that is accurate, safer, and faster than the traditional approach but much more expensive; therefore, not all authorities can afford them. To avoid these challenges, a modern, real-time, cost-effective approach is proposed to ensure the efficient and fast process of pothole maintenance. This paper presents a Deep Learning model trained using the You Only Look Once (YOLO) algorithm to capture potholes and estimate their dimensions and locations using only built-in vehicle technologies. The model attained 93.0 % precision, 91.6 % recall, 87.0 % F1-score, and 96.3 % mAP. A statistical analysis of the on-site test results indicates that the results are significant at a 5 % level, with a p-value of 0.037. This approach provides an economical and faster way of monitoring road surface conditions.

Keywords

• YOLOv5
• Vehicle built-in technologies
• Pavement condition monitoring
• Pothole size
• Computer vision
• Pavement maintenance