Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911315887390720 |
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| author | Stropeni, Arianna Borsatti, Francesco Barusco, Manuel Pezze, Davide Dalle Fabris, Marco Susto, Gian Antonio |
| author_facet | Stropeni, Arianna Borsatti, Francesco Barusco, Manuel Pezze, Davide Dalle Fabris, Marco Susto, Gian Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression Stropeni, Arianna Borsatti, Francesco Barusco, Manuel Pezze, Davide Dalle Fabris, Marco Susto, Gian Antonio Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.07119 |