Research on Multi-Scale Feature Extraction and Working Condition Classification Algorithm of Lead-Zinc Ore Flotation Foam  
[PERSON]  
1 School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China 1  
[PERSON]  
1 School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China 1  
[PERSON]  
2 Energy Storage Technology Engineering Research Center, North China University of Technology, Beijing 100144, China 2  
[PERSON]  
3 Guangi China Tin Group Co., Ltd., liuzhou 545006, China 3  
Correspondence: ziahouhilin07158163.com; Tel. : +86-150-5509-4955  
surface reflect the concentration of the mineral; the size of the foam reflects the possibility of adhesion between the mineral particles and the foam, and the smaller the foam, the greater the possibility of the useful minerals being adhered. The color of the foam also reflects the information of the mineral [3,4]. The traditional flotation condition recognition method mainly extracts features such as color, size, shape and speed of the foam surface [5], and then inputs the extracted features into the classification for condition recognition; however, such methods are not stable in feature extraction, and the features extracted from images of different quality differ, so they have a large impact on recognition accuracy and are not robust. CNN can be driven directly from the image set between features and targets, and can perform deep learning to extract image features efficiently [6]. Therefore, CNN has been introduced into the foam flotation process recognition. [PERSON] et al. [7,8] firstly applied CNN to the feature extraction of flotation foam and the prediction classification of fine ore, which greatly improved the recognition accuracy compared with the traditional flotation condition identification method. [PERSON] et al. [9] proposed a machine vision technology to accurately measure the lamellar rupture rate on the foam surface. [PERSON] et al. [10] segmented the foam image then extracted the features of the segmented bubble image through CNN, and identified the working conditions by counting the frequency distribution of various bubbles. These methods extracted the features of a single image and ignored the local details of multi-scale image features, so their recognition efficiency was low. [PERSON] et al. [11] proposed a mode recognition method utilizing bimodal CNN feature extraction and self-adaptive transfer learning based on AlexNet model. The authors optimized the model's parameters by using an improved quantum Wolf pack algorithm. [PERSON] et al. [12] proposed a transfer learning method based on CNN, and adopted SVM to generate an automatic recognition model. [PERSON] et al. [13] proposed a method to identify antimony flotation performance states based on a lightweight convolution vision transformer, and introduced a lightweight convolution neural network module and sub-module of lightweight neural network ModileNetv2 which could better capture flotation foam characteristics. Although the above methods use a convolutional neural network to extract rich foam detailed texture features, they ignore the correlation between each individual foam. In addition, the correlation measure between each pixel and the points in its surrounding neighbors in the whole image is not reflected. Meanwhile, feature extraction using CNN is incomplete and ignores the influence of high-frequency features. In recent years, the development of the multi-scale geometric analysis method has provided a new idea for image edge detection. The NSST not only has the features of multi-scale, multi directional capabilities, translation fidelity, and anisotropic characteristics, but also has high computational efficiency and unlimited decomposition methods. It can be applied to inhomogeneous foam distribution and boundary detection of different thicknesses. It can also reduce the noise level.