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

Author:

Zhang Yuan,Han Tao,Wei BingORCID,Hao Kuangrong,Gao Lei

Abstract

AbstractTextile 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.

Funder

the Fundamental Research Funds for the Central Universities

Shanghai Sailing Program

Natural Science Foundation of Shanghai

the Key Research Projects of China Telecom

Shanghai Pujiang Program

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence

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