CrashSnap: AI-Powered Vehicle Damage Detection and Intelligent Insurance Claim Processing System.
Year: 2026
Type: conference-paper
Abstract: Road accidents are increasing rapidly and generate a large number of vehicle insurance claims every year. Conventional claim processing depends on manual inspection, paperwork verification, and delayed communication, resulting in slow settlement and inconsistent decisions. This paper presents CrashSnap, an Artificial Intelligence based vehicle damage assessment and insurance claim automation platform. The proposed system uses the YOLOv8 deep learning model to detect vehicle damages, classify damage categories, localize affected regions, estimate severity, and accelerate insurance decisions. CrashSnap integrates three major modules: User Module, Insurance Company Module, and Administrative Module. Users can upload damaged vehicle images and receive instant AI-generated reports. Insurance companies can review claims, approve or reject requests, and generate settlement reports. Experimental implementation shows that the system reduces manual effort, increases transparency, improves customer experience, and achieves approximately 82% to 83% damage detection accuracy. The platform provides a scalable solution for future smart insurance ecosystems.
Authors: BINU, Rinu, P M, Shamas, K M, Sheena
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7156347914
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