Deployment of deep learning-based anomaly detection systems: challenges and solutions

Ježek, Štěpán; Burget, Radim

Deployment of deep learning-based anomaly detection systems: challenges and solutions

Files

207-eeict-2024-II.pdf(13.1 MB)

Date

2024

Authors

Ježek, Štěpán

Burget, Radim

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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Abstract

Visual anomaly detection systems play an important role in various domains, including surveillance, industrial quality control, and medical imaging. However, the deployment of such systems presents significant challenges due to a wide range of possible scene setups with varying number of devices and high computational requirements of deep learning algorithms. This research paper investigates the challenges encountered during the deployment of visual anomaly detection systems for industrial applications and proposes solutions to address them effectively. We present a model use case scenario from real-world manufacturing quality control and propose an efficient distributed system for deployment of the defect detection methods in manufacturing facilities. The proposed solution aims to provide a general framework for deploying visual defect detection algorithms base on deep neural networks and their high computational requirements. Additionally, the paper addresses challenges related the whole process of automated quality control, which can be performed with varying number of camera devices and it mostly requires interaction with other factory services or workers themselves. We believe the presented framework can contribute to more widespread use of deep learning-based defect detection systems, which may provide valuable feedback for further research and development.

Keywords

deep learning, defect detection, system design, algorithm deployment, image processing, distributed systems

Citation

Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 207-211. ISBN 978-80-214-6230-4
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence