Article
Pothole Detection and Road Damage Analysis Using YOLOv8 Deep Learning
Road surface deterioration, particularly potholes, poses significant hazards to public safety, increases vehicle maintenance expenditure, and impedes efficient transportation. Traditional inspection strategies relying on manual surveys and complaint-based reporting are reactive, labor-intensive, and unable to provide systematic, real-time coverage of large road networks. This paper presents an automated pothole detection and road damage analysis framework that leverages the YOLOv8 convolutional object detection architecture, integrated with a Flask-based web application, to perform real-time identification and severity classification of potholes from road images, pre-recorded videos, and live webcam streams. The system preprocesses visual inputs using OpenCV, executes inference through a fine-tuned YOLOv8 model, annotates detected regions with bounding boxes and confidence scores, and subsequently classifies each detection into High, Medium, or Low severity based on the proportional area of the pothole relative to the total image frame. Experimental evaluation on the RDD2020 road damage dataset demonstrates progressive reduction in training loss over fifty epochs, with a final mean average precision (mAP@0.5) of approximately 0.57 and a precision of 0.63. The web interface facilitates result visualization, statistical summarization, and JSON-format report export. The proposed system provides a scalable, cost-effective, and accessible solution for intelligent road infrastructure monitoring, with direct applicability to smart city and municipal road maintenance operations.
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