NSST域改进ORB的泡沫流动特征提取及加药状态识别  

Dosing status identification and froth flow feature extraction based on improved ORB in NSST domain

作　　者：廖一鹏[1] 陈诗媛 杨洁洁[1] 王志刚 王卫星[1] LIAO Yi-peng;CHEN Shi-yuan;YANG Jie-jie;WANG Zhi-gang;WANG Wei-xing(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,China;Fujian Jindong Mining Co.Ltd.,Sanming 365101,China)

机构地区：[1]福州大学物理与信息工程学院,福建福州350108 [2]福建金东矿业股份有限公司,福建三明365101

出　　处：《光学精密工程》2020年第12期2684-2699,共16页Optics and Precision Engineering

基　　金：国家自然科学基金资助项目(No.61471124,No.61601126);福建省自然科学基金资助项目(No.2019J01224);福建省中青年教师教育科研项目资助(No.JT180056)。

摘　　要：针对浮选泡沫表面图像动态变化、光照影响、噪声干扰导致流动特征难于提取的问题,提出了一种在NSST域改进ORB的泡沫流动特征提取方法,并应用于浮选加药状态识别。对相邻两帧泡沫图像NSST分解,对多尺度高频子带先通过尺度相关系数去除噪声再分为多个内层和外层,在各内层通过方向模极大值检测提取兴趣点,然后在本层和上下层通过非极大值抑制提取特征点,采用多尺度BRIEF描述子对特征点描述,结合泡沫的运动趋势动态调整搜索的匹配区域,根据匹配结果计算泡沫流动特征。最后,构建行列自编码极限学习机对泡沫形态、尺寸分布特征和流动特征进行融合,然后通过自适应随机森林对加药状态分类识别。实验结果表明,改进的ORB受噪声和光照影响小,流动特征检测精度和效率较现有方法有较大提高,能准确地表征不同加药状态下泡沫表面的流动特性,加药状态的平均识别精度达97.85%,较现有文献方法有较大提升,为后续的加药量优化控制奠定基础。A froth-flow feature detection method based on an improved ORB in the NSST domain was developed and applied to flotation dosing state recognition to solve the problems of continuous movement,light effects,and noise interference of flotation surface images,which lead to difficulties in flow feature detection.First,two adjacent froth images were decomposed through NSST.Multiscale high-frequency sub-bands were denoised using a scale correlation coefficient and then divided into multiple inner and outer layers.The points of interest were subsequently extracted through modulus maxima detection in each inner layer,and the feature points were extracted through non-maximum suppression between the upper and lower layers.Second,a multiscale BRIEF descriptor was adopted to describe these feature points,the search matching area was dynamically adjusted according to the movement trend of the bubbles.The froth-flow features were then calculated based on the matching results.Finally,a line-and-column autoencoder extreme learning machine was constructed to fuse the foam shape,size distribution,and flow features,and the dosing state was recognized by the adaptive random forest method.The experimental results showed that the improved ORB was slightly affected by noise and illumination.The flow feature detection efficiency and the detection accuracy were significantly better than those of existing methods.The proposed method could characterize the flow characteristics of the froth surface accurately in different dosing states.The average accuracy of dosing state recognition reached 97.85%,which was significantly higher than those of existing methods.This study lays a foundation for future research on dosing quantity optimization control.

关 键 词：浮选泡沫图像 流动特征提取 ORB 非下采样剪切波变换 行列自编码极限学习机 自适应随机森林 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]