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- Publisher Website: 10.1109/INDIN45582.2020.9442236
- Scopus: eid_2-s2.0-85111122630
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Conference Paper: Edge-cloud collaborative fabric defect detection based on industrial internet architecture
Title | Edge-cloud collaborative fabric defect detection based on industrial internet architecture |
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Authors | |
Keywords | fabric defect detection deep learning edge-cloud transfer learning |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001443 |
Citation | 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), Warwick, UK, 20-23 July 2020, p. 483-487 How to Cite? |
Abstract | Aiming to improve the adaptability of fabric defect detection, this paper proposes an “edge-cloud” collaborative fabric defect detection architecture that contains edge layer, platform layer, and application layer. In the edge layer, the fabric defect detection machine is able to realize the collection and detection of fabric images data. In the platform layer, the cloud platform that integrates memory computing, parallel storage, and a relational library is designed to realize the efficient storage and analysis of fabric data. In the application layer, a deep learning fabric defect detection algorithm is designed to recognize the defect patterns. The interaction between the cloud platform and the detection device is designed to adaptively adjust the detection algorithm. The closed-loop optimization is achieved by implementing “edge-cloud” architecture that the fabric pictures are captured and analyzed for fast detection algorithm in edge devices. The captured data is stored and monitored by the cloud platform. The cloud platform adjusts the edge detection algorithm by transfer learning, which can adapt to the changing environment. A case study illustrates that the proposed edge-cloud collaborative fabric defect detection can achieve better dynamic adaptability. |
Persistent Identifier | http://hdl.handle.net/10722/301436 |
ISSN | 2020 SCImago Journal Rankings: 0.195 |
DC Field | Value | Language |
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dc.contributor.author | Zhao, SX | - |
dc.contributor.author | Wang, JL | - |
dc.contributor.author | Zhang, J | - |
dc.contributor.author | Bao, JS | - |
dc.contributor.author | Zhong, R | - |
dc.date.accessioned | 2021-07-27T08:11:02Z | - |
dc.date.available | 2021-07-27T08:11:02Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), Warwick, UK, 20-23 July 2020, p. 483-487 | - |
dc.identifier.issn | 1935-4576 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301436 | - |
dc.description.abstract | Aiming to improve the adaptability of fabric defect detection, this paper proposes an “edge-cloud” collaborative fabric defect detection architecture that contains edge layer, platform layer, and application layer. In the edge layer, the fabric defect detection machine is able to realize the collection and detection of fabric images data. In the platform layer, the cloud platform that integrates memory computing, parallel storage, and a relational library is designed to realize the efficient storage and analysis of fabric data. In the application layer, a deep learning fabric defect detection algorithm is designed to recognize the defect patterns. The interaction between the cloud platform and the detection device is designed to adaptively adjust the detection algorithm. The closed-loop optimization is achieved by implementing “edge-cloud” architecture that the fabric pictures are captured and analyzed for fast detection algorithm in edge devices. The captured data is stored and monitored by the cloud platform. The cloud platform adjusts the edge detection algorithm by transfer learning, which can adapt to the changing environment. A case study illustrates that the proposed edge-cloud collaborative fabric defect detection can achieve better dynamic adaptability. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001443 | - |
dc.relation.ispartof | IEEE International Conference on Industrial Informatics | - |
dc.rights | IEEE International Conference on Industrial Informatics. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | fabric defect detection | - |
dc.subject | deep learning | - |
dc.subject | edge-cloud | - |
dc.subject | transfer learning | - |
dc.title | Edge-cloud collaborative fabric defect detection based on industrial internet architecture | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Zhong, R: zhongzry@hku.hk | - |
dc.identifier.authority | Zhong, R=rp02116 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/INDIN45582.2020.9442236 | - |
dc.identifier.scopus | eid_2-s2.0-85111122630 | - |
dc.identifier.hkuros | 323611 | - |
dc.identifier.spage | 483 | - |
dc.identifier.epage | 487 | - |
dc.publisher.place | United States | - |