Flotation condition recognition based on multi-scale convolutional neural network and LBP algorithm

Jiang Xiaoping; Liu Junwei; Wang Lele; Lei Zhenbin; Hu Mingzhen

doi:10.19606/j.cnki.jmst.2023.02.007

Volume 8 Issue 2

Mar. 2023

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Article Navigation > Journal of Mining Science and Technology > 2023 > 8(2): 202-212

Jiang Xiaoping, Liu Junwei, Wang Lele, Lei Zhenbin, Hu Mingzhen. Flotation condition recognition based on multi-scale convolutional neural network and LBP algorithm[J]. Journal of Mining Science and Technology, 2023, 8(2): 202-212. doi: 10.19606/j.cnki.jmst.2023.02.007

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Flotation condition recognition based on multi-scale convolutional neural network and LBP algorithm

doi: 10.19606/j.cnki.jmst.2023.02.007

1.
School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
Guangxi China Tin Group Co.LTD, Liuzhou Guangxi 545006, China

Received Date: 2022-05-03
Rev Recd Date: 2022-09-25
Publish Date: 2023-03-30

Abstract

Abstract

Aiming at the problems of difficult detection, low recognition efficiency and strong subjectivity of foam flotation dosing state, a flotation condition recognition method of nuclear random-weight neural networks(K-RVFLNs)combining multi-scale CNN characteristics and improved local binary patterns(LBP)calculation methods is proposed.Firstly, non-downsampling Shearlet multi-scale decomposition(NSST)is performed on the bubble flotation image, the original image is decomposed into different frequency scales, and a multi-channel CNN network is designed to extract features from the multi-scale image.By improving the LBP algorithm to extract features as a supplement, the image features extracted by CNN are fused with LBP features; Finally, the classification decision is made by mapping to a higher dimensional space by the nuclear stochastic right neural network, and the accurate identification of flotation dosing state is realized.Experimental results show that the method of multi-scale CNN and LBP-TOP feature fusion is 5.34 % higher than that of the traditional LBP algorithm, 3.76 % higher than that of single CNN feature recognition method, and the accuracy of flotation condition classification is as high as 96.38 % in combination with K-RVFLNs, and the recognition accuracy and stability are greatly improved compared with the existing methods, and this method reduces manual intervention and is conducive to improving production efficiency.
- image processing,
- convolutional neural networks,
- non-downsampled shearlet transform,
- local binary mode,
- random vector functional-link networks

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References

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Flotation condition recognition based on multi-scale convolutional neural network and LBP algorithm

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