Intelligent Vehicle Damage Assessment & Cost Estimator for Insurance Companies

The accurate detection and estimation of vehicle damage are crucial for efficient processing in the insurance industry. This paper presents an innovative approach that leverages the YOLOv5 (You Only Look Once, version 5) deep learning model to automate damage detection on vehicles from digital images. The YOLOv5 model is renowned for its speed and accuracy in object detection tasks, making it an ideal choice for real-time applications. We train the model on a comprehensive dataset comprised of images featuring various types of vehicle damage. Post-detection, the system estimates the repair cost based on the type and extent of damage identified, integrating historical data on repair costs. Our results show a significant improvement in damage detection accuracy and reliability compared to traditional methods. Additionally, the proposed system provides cost estimations with a high degree of precision, aiding in the streamlining of claims processing and reducing manual labor. This paper details the methodology, experimental setup, results, and potential future enhancements in the domain of automated vehicle damage assessment.

Keywords: Vehicle Damage Detection, YOLOv5, Deep Learning