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
Hlavní autoři: Hao, Zhiqiang, Wang, Zhigang, Bai, Dongxu, Tong, Xiliang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media SA 01.07.2022
Frontiers Media S.A
Témata:
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
deep learning
model compression
structured pruning
defect detection
semantic segmentation
ISSN:2296-4185, 2296-4185
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Shrnutí: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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Reviewed by: Guanbing Cheng, Civil Aviation University of China, Tianjin, China
This article was submitted to Bionics and Biomimetics, a section of the journal Frontiers in Bioengineering and Biotechnology
Weichao Guo, Shanghai Jiao Tong University, China
Edited by: Zhihua Cui, Taiyuan University of Science and Technology, China
ISSN:2296-4185
2296-4185
DOI:10.3389/fbioe.2022.945248