Surface Defect Segmentation Algorithm of Steel Plate Based on Geometric Median Filter Pruning
Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering...
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| Vydáno v: | Frontiers in bioengineering and biotechnology Ročník 10; s. 945248 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
Frontiers Media SA
01.07.2022
Frontiers Media S.A |
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| ISSN: | 2296-4185, 2296-4185 |
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| Abstract | Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering for structured pruning and compression of defect segmentation detection networks on the basis of structured pruning. Through experimental comparisons and optimizations, the proposed optimization algorithm can greatly reduce the network parameters and computational effort to achieve effective pruning of the defect detection algorithm for steel plate surfaces. |
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| AbstractList | Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering for structured pruning and compression of defect segmentation detection networks on the basis of structured pruning. Through experimental comparisons and optimizations, the proposed optimization algorithm can greatly reduce the network parameters and computational effort to achieve effective pruning of the defect detection algorithm for steel plate surfaces. Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering for structured pruning and compression of defect segmentation detection networks on the basis of structured pruning. Through experimental comparisons and optimizations, the proposed optimization algorithm can greatly reduce the network parameters and computational effort to achieve effective pruning of the defect detection algorithm for steel plate surfaces.Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering for structured pruning and compression of defect segmentation detection networks on the basis of structured pruning. Through experimental comparisons and optimizations, the proposed optimization algorithm can greatly reduce the network parameters and computational effort to achieve effective pruning of the defect detection algorithm for steel plate surfaces. |
| Author | Bai, Dongxu Hao, Zhiqiang Wang, Zhigang Tong, Xiliang |
| AuthorAffiliation | 1 Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education , Wuhan University of Science and Technology , Wuhan , China 3 Precision Manufacturing Research Institute , Wuhan University of Science and Technology , Wuhan , China 2 Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering , Wuhan University of Science and Technology , Wuhan , China 4 Research Center for Biomimetic Robot and Intelligent Measurement and Control , Wuhan University of Science and Technology , Wuhan , China |
| AuthorAffiliation_xml | – name: 3 Precision Manufacturing Research Institute , Wuhan University of Science and Technology , Wuhan , China – name: 2 Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering , Wuhan University of Science and Technology , Wuhan , China – name: 4 Research Center for Biomimetic Robot and Intelligent Measurement and Control , Wuhan University of Science and Technology , Wuhan , China – name: 1 Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education , Wuhan University of Science and Technology , Wuhan , China |
| Author_xml | – sequence: 1 givenname: Zhiqiang surname: Hao fullname: Hao, Zhiqiang – sequence: 2 givenname: Zhigang surname: Wang fullname: Wang, Zhigang – sequence: 3 givenname: Dongxu surname: Bai fullname: Bai, Dongxu – sequence: 4 givenname: Xiliang surname: Tong fullname: Tong, Xiliang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35845429$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2022 Hao, Wang, Bai and Tong. 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2022 Hao, Wang, Bai and Tong. 2022 Hao, Wang, Bai and Tong |
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| Keywords | deep learning model compression structured pruning defect detection semantic segmentation |
| Language | English |
| License | Copyright © 2022 Hao, Wang, Bai and Tong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| SubjectTerms | Algorithms Bioengineering and Biotechnology Compression Decomposition Deep learning defect detection Defects Design Knowledge Learning Methods model compression Neural networks Optimization Real time Segmentation semantic segmentation Sparsity structured pruning |
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| Title | Surface Defect Segmentation Algorithm of Steel Plate Based on Geometric Median Filter Pruning |
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