Industrial inspection needs zero-shot anomaly detection (ZSAD) that remains useful under edge deployment constraints. Recent methods often rely on ViT-L foundation backbones (~300M parameters), which exceed the memory and operator budget of typical embedded hardware. We study this regime through EdgeZSAD, a compact reference system built around a TinyViT-21M-512 backbone, an asymmetric global-local readout (EdgeGLR), and a reproducible source-side training recipe (Real-IAD-DR). We train a single checkpoint in a source-trained, target-unseen protocol and evaluate it across six industrial benchmarks. Across three independent runs, the resulting model reaches an average image AUROC of 91.6 on MVTec-AD and 88.2 on VisA, while remaining directly deployable on Jetson Orin Nano Super (TensorRT FP16) and RB5 Gen2 (QNN GPU FP16). Across the six device-rescored benchmarks, image-AUROC drift stays below 0.2 points, indicating that the exported graph preserves host-side ranking behavior in the evaluated deployment setting.

翻译：工业检测需要零样本异常检测（ZSAD），使其在边缘部署约束下仍保持实用性。现有方法通常依赖ViT-L基础骨干网络（约3亿参数），这超出了典型嵌入式硬件的内存和算子预算。我们通过EdgeZSAD研究该领域——这是一种基于TinyViT-21M-512骨干网络、非对称全局-局部读取模块（EdgeGLR）和可复现源端训练策略（Real-IAD-DR）构建的紧凑参考系统。我们采用源端训练-目标端未知协议训练单一检查点，并在六个工业基准上评估其性能。在三次独立运行中，所得模型在MVTec-AD上达到91.6的平均图像AUROC，在VisA上达到88.2，同时可直接部署于Jetson Orin Nano Super（TensorRT FP16）和RB5 Gen2（QNN GPU FP16）。在六个设备重评分基准上，图像AUROC波动不超过0.2个百分点，表明导出图在评估部署环境下能保持宿主的排序行为。