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° 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 \(\approx\! 300\,\text{ms}\). 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 \(\mathbf{3.3}\) 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.

翻译：确保现代生产线下线的每辆车均符合正确的**变体**规格且无可见缺陷，是一项日益复杂的挑战。本文提出**自动化车辆检测（AVI）**平台，这是一种端到端的**多视角**感知系统，将深度学习检测器与语义规则引擎相结合，实现实时**变体感知**的质量控制。十一个同步摄像头采集每辆车的完整360°环视图像；任务专用视角随后被路由至专用模块：YOLOv8用于部件检测，EfficientNet用于内燃机/电动汽车分类，Gemini-1.5 Flash用于吉祥物光学字符识别，YOLOv8-Seg用于划痕与凹陷分割。视角感知融合层对证据进行标准化处理，同时基于车辆识别码的条件规则引擎将检测到的特征与预期清单进行比对，在约300毫秒内生成可解释的通过/失败报告。在包含四种不同车型的原始设备制造商车辆数据集及公开划痕/凹陷图像的混合数据集上，AVI实现了**93%**的验证准确率、**86%**的缺陷检测召回率，并保持**每分钟3.3辆**的处理速度，大幅超越单视角或无分割基线方法。据我们所知，这是首个公开报道的在可部署工业汽车场景中统一多摄像头特征验证与缺陷检测的系统。