EdgeZSAD: Practical Zero-Shot Anomaly Detection on Edge Devices

• URL: http://arxiv.org/abs/2606.16119v1
• Date: Mon, 15 Jun 2026 02:17:46 GMT
• Title: EdgeZSAD: Practical Zero-Shot Anomaly Detection on Edge Devices
• Abstract summary: Industrial inspection needs zero-shot anomaly detection (ZSAD) that remains useful under edge deployment constraints.<n>We study this regime through EdgeZSAD, a compact reference system built around a TinyViT-21M-512 backbone.<n>We train a single checkpoint in a source-trained, target-unseen protocol and evaluate it across six industrial benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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