B. Ganesh1, K. Jahnavi2, B. Vamsi Naidu3, A. Venkatesh4, P. Venkatesh5 03 3(1) 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 complaintbased reporting are reactive laborintensive and unable to provide systematic realtime 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 Flaskbased web application to perform realtime identification and severity classification of potholes from road images prerecorded videos and live webcam streams The system preprocesses visual inputs using OpenCV executes inference through a finetuned 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 mAP05 of approximately 057 and a precision of 063 The web interface facilitates result visualization statistical summarization and JSONformat report export The proposed system provides a scalable costeffective and accessible solution for intelligent road infrastructure monitoring with direct applicability to smart city and municipal road maintenance operations Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026