Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression
This addresses the challenge of minimizing operational costs for industrial IoT deployments, though it is incremental as it builds on existing compression methods.
The study tackled the problem of deploying visual anomaly detection in IoT environments with limited computational power and bandwidth by evaluating data compression techniques, achieving up to 80% reduction in end-to-end inference time with minimal loss in accuracy on the MVTec AD benchmark.
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.