人工智能
模式识别(心理学)
卷积(计算机科学)
卷积神经网络
计算机科学
图形
织物
深度学习
人工神经网络
机器学习
材料科学
理论计算机科学
复合材料
作者
Yuan Zhang,Tao Han,Bing Wei,Kuangrong Hao,Lei Gao
标识
DOI:10.1007/s40747-023-01070-y
摘要
Abstract 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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