Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression (2505.07119v2)

Abstract: 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.

Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression

The paper "Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression" addresses a significant challenge in deploying Visual Anomaly Detection (VAD) systems within Internet of Things (IoT) environments. As industrial automation increasingly relies on distributed IoT frameworks, the limitations of computational power and bandwidth on edge devices necessitate efficient data processing and transmission strategies. This paper proposes a resource-aware VAD framework tailored for such settings, aiming to optimize the trade-off between detection accuracy and system latency.

Framework Overview

The proposed framework is designed for Industrial IoT (IIoT) applications, where minimizing communication overhead and computational burden is crucial. The authors present a modular approach consisting of:

• Image Compression: Using WebP encoding, which demonstrates minimal accuracy loss, to reduce the size of transmission payloads.
• Feature Extraction and Compression: Employing lightweight CNNs like MobileNetV2 for on-device processing and utilizing random sampling and product quantization (PQ) to further compress extracted features.
• Centralized Detection: Leveraging more computationally intensive models on a central server, such as WideResNet50, to perform final feature extraction and anomaly detection using PatchCore.

Experimental Validation

The effectiveness of the framework is validated using the MVTec AD dataset, a benchmark for unsupervised anomaly detection across various object and texture categories. Key findings include:

• The WebP-based image compression strategy preserves anomaly detection performance with only a 2.6% reduction in pixel-wise F1-score compared to uncompressed images.
• Feature extraction coupled with PQ compression achieves substantial data size reduction (down to 2% of the original size) while incurring a more significant accuracy drop.
• The combined RS+WebP and RS+PQ approaches offer efficient transmission times (approximately 80% reduction) at the expense of detection performance, highlighting the necessity for careful strategy selection based on application needs.

Implications and Future Directions

The proposed VAD framework successfully demonstrates that efficient anomaly detection is achievable in bandwidth-constrained environments with little compromise on accuracy. The outlined architecture offers practical applicability in IIoT systems, where rapid deployment and real-time monitoring are critical. Future research directions include exploring adaptive compression techniques that dynamically adjust based on network conditions and integrating real-time model update mechanisms to handle evolving anomaly patterns.

In conclusion, this paper contributes to the field of IoT-enabled anomaly detection by showcasing scalable solutions that effectively balance the complexity of deep learning models with operational constraints typical in edge computing environments. The modular pipeline offers opportunities for further enhancements through novel compression algorithms and integration with advanced communication protocols.