Smart-Claim: AI-Based Vehicle Damage Detection and Estimation Using YOLO and Large Language Models
Abstract
Vehicle damage inspection is an essential step in insurance claim processing and vehicle repair management. Traditional inspection procedures rely on manual verification by surveyors, which often results in delays, human errors, and inconsistencies in repair estimation. To address these challenges, this research proposes Smart-Claim, an artificial intelligence-based system designed to automate vehicle damage detection and provide repair estimation insights using computer vision and large language models. The system utilizes the YOLO object detection model to identify damaged regions in vehicle images uploaded by users. The detected damage information is analyzed using NVIDIA Nemotron-3 API powered by the Llama 3.1 90B large language model to generate intelligent estimation insights. The application is implemented using React for the frontend, Python Flask for the backend, and MySQL for database management. Experimental results demonstrate that the proposed system significantly reduces inspection time and enhances efficiency in insurance claim evaluation.
Keywords
Artificial Intelligence, Computer Vision, Vehicle Damage Detection, YOLO, Insurance Claim Automation, Deep Learning.
Citations
IRE Journals:
Asan Nainar M , Jeevanandan B K "Smart-Claim: AI-Based Vehicle Damage Detection and Estimation Using YOLO and Large Language Models" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 1675-1679 https://doi.org/10.64388/IREV9I9-1715302
IEEE:
Asan Nainar M , Jeevanandan B K
"Smart-Claim: AI-Based Vehicle Damage Detection and Estimation Using YOLO and Large Language Models" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715302
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