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DEEP LEARNING – BASED VEHICLE DAMAGE ASSESSMENT SYSTEM
Vehicle damage assessment is an essential process in automobile insurance and repair services. Traditional inspection methods are manual, timeconsuming, and prone to errors. This paper presents a Deep Learning-Based Vehicle Damage Assessment System that uses the YOLO (You Only Look Once) object detection algorithm to automatically detect and classify vehicle damages such as dents, scratches, broken glass, and bumper damage from uploaded images. Developed using Python, Flask, OpenCV, TensorFlow/Keras, and SQLite, the system provides fast, accurate, and reliable damage detection by highlighting damaged regions with bounding boxes and labels. It reduces inspection time, minimizes human intervention, and supports efficient insurance claim processing and repair estimation, making it a practical solution for intelligent vehicle damage assessment.
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