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Original title:
Deployment of deep learning-based anomaly detection systems: challenges and solutions
Authors: Ježek, Štěpán ; Burget, Radim
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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: algorithm deployment; deep learning; defect detection; distributed systems; image processing; system design
Host item entry: Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers, ISBN 978-80-214-6230-4, ISSN 2788-1334
Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/249316
Permalink: http://www.nusl.cz/ntk/nusl-622579
The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers
Authors: Ježek, Štěpán ; Burget, Radim
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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: algorithm deployment; deep learning; defect detection; distributed systems; image processing; system design
Host item entry: Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers, ISBN 978-80-214-6230-4, ISSN 2788-1334
Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/249316
Permalink: http://www.nusl.cz/ntk/nusl-622579
The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers
Record created 2024-07-21, last modified 2024-07-21