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Proceedings Paper

Classification of common recyclable garbage based on hyperspectral imaging and deep learning
Author(s): Rui Wu; Bin Zhang; Dong-e Zhao
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Paper Abstract

The differences in material properties of various common recyclable garbage will be directly mapped to the difference in spectral characteristics. While acquiring the spectral information of the garbage, the hyperspectral imaging technology can obtain the spatial information of the garbage, and realize the rapid detection and classification of garbage by using the method of "spectral-spatial" .The classification model is established combined the spectral characteristics under the sample feature space with CNN (convolutional neural network) machine learning algorithm, and then the classification model is trained and optimized by using database training sample set. Finally, 92.10% classification accuracy is achieved after testing the sample set.

Paper Details

Date Published: 12 March 2020
PDF: 8 pages
Proc. SPIE 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1143807 (12 March 2020); doi: 10.1117/12.2540984
Show Author Affiliations
Rui Wu, North Univ. of China (China)
Bin Zhang, North Univ. of China (China)
Dong-e Zhao, North Univ. of China (China)


Published in SPIE Proceedings Vol. 11438:
2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology
Guohai Situ; Xun Cao; Wolfgang Osten, Editor(s)

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