How to Build an AI Video Car Damage Inspector

Aarnav Shah describes an AI-assisted rental-car inspection system that analyzes a user-uploaded walkaround video and produces a digitally signed, browser-verifiable PDF report of detected vehicle damage. Before upload, the app fingerprints the video and obtains an independent timestamp, then verifies the uploaded file against that certified fingerprint to establish an evidence chain. The pipeline uses a custom-trained RF-DETR-small model in Roboflow to detect damage, ByteTrack to connect detections across frames, and an optical-flow-based parallax filter to distinguish stationary surface damage from moving reflections. Gemini then evaluates only detections proposed by the vision model, identifying damage type, severity, size, and vehicle location while being permitted to reject false positives but not create new findings. The system also identifies vehicle details, signs the final report with ECDSA P-256, and applies a second timestamp to the report itself. Shah provides a GitHub repository containing the Next.js web application, local analysis pipeline, and Roboflow workflow definitions, noting that analysis can run locally while Gemini assessments use managed Roboflow credits and the architecture can later scale to hosted GPUs.

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