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

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.

Keywords

• image processing
• convolutional neural networks
• non-downsampled shearlet transform
• local binary mode
• random vector functional-link networks