摘要
采用山西省的焦煤和肥煤作为研究对象,针对目前利用煤矸石灰度信息作为判断二者依据的局限性问题,提出了一种基于CNN卷积神经网络的煤矸石自动分选系统.该系统利用构建的卷积神经网络通过对煤块和矸石图像纹理特征的多层次提取进行结果分类输出.测试结果表明,该方法不受样本数据色差的影响,可以成功的识别检测出煤块和矸石,准确率达到92%.
Taking coking coal and fat coal of Shanxi Province as the research object,aiming at the limitation of using the information of coal gangue lime degree as the basis of judging the two,an automatic separation system of coal gangue based on CNN convolution neural network is proposed.The system uses the convolution neural network to extract the texture features of the coal and gangue image and output the classification results.The test results show that the method is not affected by thecolor difference of sample data,and can successfully identify and detect coal and gangue,with an accuracy of 92%.
作者
王莉
于国防
沈慧宇
田波
WANG Li;YU Guofang;SHEN HuiYu;TIAN Bo(School ofInformation and Electronics Engineering,Jiangsu Vocational Institute of Architectural Technology,Xuzhou,Jiangsu 221116,China;School of Electronic and Information Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221006,China;Department of Information Management,Chienkuo Technology University,Taiwan,China;Shanxi Lanxian Coking Coal Company)
出处
《江苏建筑职业技术学院学报》
2019年第4期35-39,共5页
Journal Of Jiangsu Vocational Institute of Architectural Technology
关键词
卷积神经网络
深度学习
煤矸石分选
灰度信息
convolutional neural network
deep learning
coal gangue sorting
gray information
