摘要
为了提高煤与矸石的识别率,运用自制的煤矸自动分选装置,研究了煤与矸石图像的自动识别技术,介绍了煤与矸石图像的灰度特征以及基于灰度共生矩阵的煤与矸石纹理特征。利用灰度特征的均值和纹理特征的能量、熵、对比度,相关性构造归一化特征向量,最后结合BP神经网络进行识别分类,试验分析了不同学习速率对识别率的影响。结果表明:基于BP神经网络的纹理和灰度特征的综合分类方法提高了煤与矸石的识别率;选取合适的学习速率在提高BP神经网络学习速度的同时还提高了识别率,识别率达87.5%。
In this paper,a self-made automatic coal gangue sorting device was employed to investigate the automatic image recognition technology on coal and gangue. The gray features of coal and gangue images and their texture features based on gradation co-occurrence matrix were introduced. Besides,the normalized eigenvectors are constructed by using the energy, entropy, contrast, and correlation of the grayscale features and texture features. Finally, the BP neural network was used to identify and clarify, and the influence of different learning rates on the recognition rate was experimentally analyzed. The results showed that the comprehensive classification method of texture and gray features based on BP neural network improved the recognition rate of coal and gangue; suitable learning rate improved the learning speed of BP neural network and the recognition rate which reached to 87. 5%.
作者
鲁恒润
王卫东
徐志强
吕子奇
李群
LU Heng-run;WANG Wei-dong;XU Zhi-qiang;LV Zi-qi;LI Qun(School of Chemical and Environment Engineering,China University of Mining and Technology（Beijing）,Beijing 100083,China)
出处
《煤炭工程》
北大核心
2018年第8期137-140,共4页
Coal Engineering
基金
国家自然科学基金资助项目(51574251)
中国矿业大学(北京)"越崎青年学者"和"越崎学者"资助计划
关键词
煤
矸石
机器视觉
特征提取
BP神经网络
coal
gangue
machine vision
feature extraction
BP neural network
