GAO Xinyu, LI Bo, WANG Luyao, LI Lianjie, WANG Xuewen

(College of Mechanical and Vehicle Engineering； Shanxi Provincial Key Laboratory of Coal Mining Equipment, Taiyuan University of Technology， Taiyuan 030024，China)

Abstract: A platform of coal-gangue separation was set up in the laboratory according to the actual working conditions. Adopting object detection network to identify coal and gangue online and according to the shape and size of coal gangue during sorting, the feature pyramid in the object detection network was set as three scales, and the shape and size of the anchor were determined. Comparing the AP (average precision) of the validation set under 10 IOU (intersection over union) thresholds, and a dynamic test was carried out on the separation platform built. The results show that the classification and positioning effect of the object detection network was the best when the IOU was 0.8, and the precision and recall of dynamic identification can reach more than 95%.

Keywords: coal-gangue separation; image processing; deep learning; object detection

中图分类号：TD94; TD67

文献标志码：A

文章编号：1008-5548(2021)04-0077-07

DOI:10.13732/j.issn.1008-5548.2021.04.010

收稿日期： 2021-04-23， 修回日期：2021-05-01，在线出版时间：2021-06-01 09:55。

基金项目：山西省重点研发计划项目，编号：201903D121074。

第一作者简介：高新宇(1995—)，男，硕士研究生，研究方向为煤矸石智能分选。E-mail： 813358884@qq.com。

通信作者简介：王学文(1979—)，男，博士，教授，博士生导师，研究方向为机械设计及理论。E-mail： wxuew@163.com。