A Survey on Vehicle Damage Detection using Deep Learning Towards Intelligent Insurance
Vehicle damage detection plays a vital role in assessing damage severity and estimating repair costs, which are essential for efficient insurance claim processing. However, this task is still predominantly manual, making it time-consuming and prone to errors or fraud due to human involvement. This paper presents an experimental comparative analysis of state-of-the-art object detection models for identifying and classifying vehicle damage. The evaluated models include RT-DETR, YOLOv12n, YOLOv11n, and YOLOv8n. Experimental results indicate that the models perform differently in detecting small and complex damage areas and in maintaining stable performance under varying lighting and viewing conditions. RT-DETR achieves the highest mean Average Precision (mAP) of 54.1%, outperforming YOLOv11n (47.0%), YOLOv8n (46.2%), and YOLOv12n (48.1%). This analysis highlights the potential of object detection models in the domain of vehicle damage assessment, contributing to cost reduction, improved transparency, and more efficient insurance claim workflows.
@inproceedings{tran2025survey,
title={A Survey on Vehicle Damage Detection using Deep Learning Towards Intelligent Insurance},
author={Tran, Doan-Hieu and Hoang, Van-Dung and Nguyen, Dung and Pham, Thanh-An and Le, Van-Tuong-Lan},
booktitle={2025 17th International Conference on Human System Interaction (HSI)},
pages={1--6},
year={2025},
organization={IEEE}
}