mlsanityData to 14 Oct 2026

Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression

arXiv 2505.07119 ↗First posted 11 May 2025cs.CV cs.AIcited by 1

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Abstract (arXiv)

Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing operational costs is essential. Deploying deep learning models within Internet of Things (IoT) environments introduces specific challenges due to limited computational power and bandwidth of edge devices. This study investigates how to perform VAD effectively under such constraints by leveraging compact, efficient processing strategies. We evaluate several data compression techniques, examining the tradeoff between system latency and detection accuracy. Experiments on the MVTec AD benchmark demonstrate that significant compression can be achieved with minimal loss in anomaly detection performance compared to uncompressed data. Current results show up to 80% reduction in end-to-end inference time, including edge processing, transmission, and server computation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 11 May 2025.

Builds on

16 of 29 references.

References · 16

  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  2. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  3. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  4. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  5. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  6. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  7. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  8. 2018Deep feature compression for collaborative object detectioncited by 48
  9. 2024PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edgecited by 16
  10. 2022IoT Anomaly Detection Methods and Applications: A Surveycited by 14
  11. 2024Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observabilitycited by 4
  12. 2022Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Featurescited by 3
4 more
  1. 2025Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detectioncited by 2
  2. 2021Secure Consensus via Objective Coding: Robustness Analysis to Channel Tamperingcited by 1
  3. 2023On the impact of regularization in data-driven predictive controlcited by 1
  4. 2023A Robustness Analysis to Structured Channel Tampering Over Secure-by-Design Consensus Networkscited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025MoViAD: A Modular Library for Visual Anomaly Detectioncited by 0