Pothole Detection Using Yolo and Computer Vision

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Road infrastructure is the infrastructural core and has an important function in transportation and economic growth activities. Nevertheless, potholes have a profound effect on the safety of driving, as they lead to vehicle repairs and operating costs, and on the effectiveness of road systems. State-of-the- art pothole detection methods involve either manual inspection of the road or sensor- based methods, which are inefficient, labor-intensive and expensive. Because of this, there is an increasing demand for automatic intelligent systems to identify potholes automatically and effectively in order to improve road maintenance and roadside safety. This paper introduces an intelligent pothole detection system, using combination of deep learning and real-time video processing for effective pothole detection. The system is based on a state-of-the-art object detection algorithm (YOLO (You Only Look Once) model) to identify potholes in real-time video streams. The YOLO model facilitates real-time high-accuracy pothole detection and localization, which can support automated log and analysis of freeway conditions. The suggested system is an automated, time- saving, economical tool that helps local governments to detect and repair road defects. Keywords—Keywords: computer vision, YOLO, machine learning, CNN, real-time detection, geolocation- based detection, video analysis.