Study on the Hyper-Spectral Image Characteristics of Liquid Stains in Waste Textiles

Li, Xi; Jin, Zhijun; Qiu, Yiwei; Jiang, Jingjing; Jin, Xiaoke

doi:10.1109/iscid52796.2021.00035

2021 14th International Symposium on Computational Intelligence and Design (ISCID) 2021

DOI: 10.1109/iscid52796.2021.00035

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Study on the Hyper-Spectral Image Characteristics of Liquid Stains in Waste Textiles

Xi Li

Zhijun Jin

Yiwei Qiu

et al.

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2022

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Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network

Yang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, aiming at the application of online rapid sorting of waste textiles, a large number of effective high-content blending data are generated by using generative adversity network to deeply mine the combination relationship of blending spectra, and A BEGAN-RBF-SVM classification model is constructed by compensating the imbalance of negative samples in the data set. Various experiments show that the model can effectively extract the spectrum of pure textile samples. The classification model has high robustness and high speed, reaches the performance of similar products in the world, and has a broad application market.

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Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network

Yang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

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Study on the Hyper-Spectral Image Characteristics of Liquid Stains in Waste Textiles

Cited by 1 publication

References 9 publications

Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network

Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network

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