Ensuring that every vehicle leaving a modern production line is built to the correct \emph{variant} specification and is free from visible defects is an increasingly complex challenge. We present the \textbf{Automated Vehicle Inspection (AVI)} platform, an end-to-end, \emph{multi-view} perception system that couples deep-learning detectors with a semantic rule engine to deliver \emph{variant-aware} quality control in real time. Eleven synchronized cameras capture a full 360{\deg} sweep of each vehicle; task-specific views are then routed to specialised modules: YOLOv8 for part detection, EfficientNet for ICE/EV classification, Gemini-1.5 Flash for mascot OCR, and YOLOv8-Seg for scratch-and-dent segmentation. A view-aware fusion layer standardises evidence, while a VIN-conditioned rule engine compares detected features against the expected manifest, producing an interpretable pass/fail report in ≈300ms. On a mixed data set of Original Equipment Manufacturer(OEM) vehicle data sets of four distinct models plus public scratch/dent images, AVI achieves \textbf{93\%} verification accuracy, \textbf{86 \%} defect-detection recall, and sustains 3.3 vehicles/min, surpassing single-view or no segmentation baselines by large margins. To our knowledge, this is the first publicly reported system that unifies multi-camera feature validation with defect detection in a deployable automotive setting in industry.
@article{arxiv.2509.26454,
title = {Multi-View Camera System for Variant-Aware Autonomous Vehicle Inspection and Defect Detection},
author = {Yash Kulkarni and Raman Jha and Renu Kachhoria},
journal= {arXiv preprint arXiv:2509.26454},
year = {2026}
}