改进纹理模糊筛选下煤矸石X射线图像处理  

X-ray image processing of coal gangue under improved texture fuzzy screening

作　　者：申利飞[1] 田子建[1] 白林绪[1] SHEN Li-fei;TIAN Zi-jian;BAI Lin-xu(School of Electromechanical and Information Engineering,China University of Mining and Technology (Beijing),Beijing 100083,China)

机构地区：[1]中国矿业大学(北京)机电与信息工程学院,北京100083

出　　处：《激光与红外》2022年第7期1090-1097,共8页Laser & Infrared

基　　金：国家自然科学基金项目(No.52074305)资助。

摘　　要：煤矿与矸石的自动分选是一种非接触、实时式的筛选过程,是煤矿生产的重要步骤,可以有效提高能源利用率并减少环境污染。但现有的煤块与煤矸石图像识别与分选技术大多采用Laplacian算子法,导致提取的图像纹理差异较小,识别效果不佳。本文以X射线图像技术为核心进行图像采集,对采集到的煤矿与矸石的图像进行滤波、二值化处理,降低图像噪点。在预处理后的图像中进行纹理提取,获取煤矿与矸石的纹理特征。利用Prewitt算子法计算目标图像的纹理度和灰度峰值,据此提出煤与矸石的识别值,确定煤石分离的灰度阈值,依据灰度阈值实现煤与矸石的分选。实验结果证明,本文所使用的Prewitt算子法提取出的煤与矸石的灰度差异更大,提高了实现煤与矸石的识别效果。Automatic separation of coal mine and waste rock is a non-contact and real-time screening process,which is an important step in coal mine production,and can effectively improve the energy utilization rate and reduce environmental pollution.However,the existing coal gangue image recognition and sorting technologies mostly use the Laplacian operator method,which leads to small differences in the texture of the extracted images and poor recognition results.This paper takes X-ray image coal as the core for image collection.The collected images of coal mine and gangue are filtered and binarized to reduce image noise.The texture extraction is performed on the pre-processed image to obtain the texture characteristics of coal gangue.The Prewitt operator method is used to calculate the texture degree and grayscale peak value of the target image.Based on this,the identification value of coal and gangue is proposed.The gray threshold value of coal sorting is determined.The sorting of coal gangue is realized according to the gray threshold value.The experimental results prove that the gray scale difference between coal and gangue extracted by the Prewitt operator method used in this paper is greater,which improves the recognition effect of coal and gangue.

关 键 词：煤矸石 纹理提取 X射线 图像识别 自动筛选 二值化 Prewitt算子法 像素灰度峰值 

分 类 号：O434[机械工程—光学工程] TP391.41[理学—光学]