基于激光散斑成像的煤矸特征提取与识别方法  

Feature Extraction and Recognition Method of Coal and Gangue Based on Laser Speckle Imaging

作　　者：李鹤群 郑予菲 杨涵夕 刘芸[1] 焦明星[1] Li Hequn;Zheng Yufei;Yang Hanxi;Liu Yun;Jiao Mingxing(School of Mechanical and Precision Instrument Engineering,Xi’an University of Technology,Xi’an 710048,Shaanxi,China)

机构地区：[1]西安理工大学机械与精密仪器工程学院,陕西西安710048

出　　处：《光学学报》2024年第21期206-215,共10页Acta Optica Sinica

基　　金：国家自然科学基金(52004213);陕西省重点研发项目(2023-YBGY-400);西安市科技计划项目(22GXFW0089)。

摘　　要：为了解决基于图像识别的煤矸分选技术中,识别准确率受图像采集环境照度影响较大、识别效果不佳的问题,提出一种基于激光散斑成像的煤矸特征提取与识别方法。针对煤矸激光散斑分布特点,提出Lab颜色空间下基于高斯金字塔的Otsu阈值分割方法,以准确分割煤矸激光散斑图像的感兴趣区域。利用灰度区域大小矩阵设计了煤矸激光散斑图像纹理特征提取方法,并结合传统特征提取方法构建煤矸激光散斑图像多维特征数据。利用所提取特征数据对支持向量机分类器进行训练,最终实现煤与矸石的准确识别。经实验验证,在多种照度下煤矸激光散斑成像的识别准确率平均值为96.05%,最高可达98.6%,标准差为1.85%。通过与现有识别方法开展对比实验,证明了本研究方法进行煤矸识别的可靠性且在不同照度环境下具有较小的识别准确率波动。Objective The separation of coal and gangue is a crucial step in coal production.Traditional methods,such as manual identification,mechanical methods,and X-ray transmission,are labor-intensive and environmentally harmful.Image based coal and gangue recognition technology,known for its high intelligence,compact equipment,and eco-friendliness,has become a research hotspot in dry coal beneficiation.However,scholars have found that camera-based image acquisition of coal and gangue is suboptimal under certain conditions,such as in dim environments or with adhesive samples,and the recognition effectiveness is unstable under varying lighting.To address these issues,we propose a method for coal and gangue feature extraction and recognition based on laser speckle imaging.Methods We analyze the characteristics of laser speckle and design a coal and gangue laser speckle imaging system.A dataset of laser speckle images under varying illuminance is constructed for experimental validation.A region-of-interest extraction method is developed to retain the target area of coal and gangue laser speckles under different lighting conditions while minimizing edge interference,thereby obtaining more accurate feature data.Feature extraction methods are designed to better capture the intra-class similarity and inter-class dissimilarity of minerals.A support vector machine(SVM)is employed to recognize coal and gangue,verifying the method’s effectiveness.The feature vector extracted from the graylevel size zone matrix(GLSZM)is input into the SVM to validate its effectiveness.We compare the recognition performance of the SVM when using the fusion of gray-level features and gray-level co-occurrence matrix(GLCM)features with that of using the fusion of these two features plus GLSZM features.This confirms the enhancement in recognition effectiveness of coal and gangue by our method.The recognition accuracy of our method is compared with prevalent coal and gangue recognition methods under various illuminance conditions,verifying its effective

关 键 词：机器视觉 煤矸识别 激光散斑 特征提取 不同照度 

分 类 号：TD94[矿业工程—选矿]