A spatial–spectral adaptive learning model for textile defect images recognition with few labeled data Article Swipe

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Yuan Zhang , Tao Han , Bing Wei , Kuangrong Hao , Lei Gao ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1007/s40747-023-01070-y

Textile defect recognition is a significant technique in the production processes of the textile industry. However, in the practical processes, it is hard to acquire large amounts of textile defect samples. Meanwhile, the textile samples with correct defect labels are rare. To address these two limitations, in this paper, we propose a novel semi-supervised graph convolutional network for few labeled textile defect recognition. First, we construct the graph convolutional network and convolution neural network to extract spectral features and spatial features. Second, the adaptive convolution structure is proposed to generate adaptive kernels according to their dynamically learned features. Finally, the spatial–spectral adaptive unified learning network (SSA-ULNet) is built for limited labeled defective samples, and graph-based semi-supervised learning is constructed. The textile defect recognition model can extract the textile image features through the image descriptors, enabling the whole network to be end-to-end trainable. To evaluate the proposed method, one public dataset and two unique self-built textile defect datasets are used to textile defect recognition. The evaluation results demonstrate that the proposed SSA-ULNet obviously outperforms existing state-of-the-art deep learning methods.

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Artificial Intelligence

Convolutional Neural Network

Theoretical Computer Science

Composite Material

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Artificial intelligence Pattern recognition (psychology) Convolution (computer science) Convolutional neural network Computer science Graph Textile Deep learning Artificial neural network Machine learning Materials science Theoretical computer science Composite material

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Type: article
Language: en
Landing Page: https://doi.org/10.1007/s40747-023-01070-y
PDF: https://link.springer.com/content/pdf/10.1007/s40747-023-01070-y.pdf
OA Status: gold
Cited By: 7
References: 37
Related Works: 10
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DOI: https://doi.org/10.1007/s40747-023-01070-y

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Title: A spatial–spectral adaptive learning model for textile defect images recognition with few labeled data

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-05-08

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Authors: Yuan Zhang, Tao Han, Bing Wei, Kuangrong Hao, Lei Gao

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Landing page: https://doi.org/10.1007/s40747-023-01070-y

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Concepts: Artificial intelligence, Pattern recognition (psychology), Convolution (computer science), Convolutional neural network, Computer science, Graph, Textile, Deep learning, Artificial neural network, Machine learning, Materials science, Theoretical computer science, Composite material

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Cited by: 7

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