矿石块度视觉识别判断方法  

Visual recognition and judgment method of ore fragmentation

作　　者：何文轩 胡健 柳小波 荆洪迪 孙效玉[1] HE Wenxuan;HU Jian;LIU Xiaobo;JING Hongdi;SUN Xiaoyu(Intelligent Mine Research Center,Northeastern University,Shenyang 110819,China;Angang Guanbaoshan Mining Co.,Ltd.,Anshan 114014,China;Digital Factory Department,Shenyang Institute of Automation Chinese Academy of Sciences,Shenyang 110016,China)

机构地区：[1]东北大学智慧矿山研究中心,辽宁沈阳110819 [2]鞍钢集团关宝山矿业有限公司,辽宁鞍山114014 [3]中国科学院沈阳自动化研究所数字工厂研究室,辽宁沈阳110016

出　　处：《中国矿业》2021年第6期100-105,共6页China Mining Magazine

基　　金：国家自然科学基金研究基金资助(编号:51674063);中央高校基础研究经费资助(编号:170104017);中央高校基础研究经费资助(编号:180701003)。

摘　　要：针对传送带矿石块度还需要人工测量的问题,提出了一种基于深度学习的矿石块度检测方法。该方法在Darknet框架下采用残差神经网络结构组成CSPDarkNet21主干特征提取网络,在考虑只需要识别判断大块矿石的条件下选用简单双向特征融合PANet作为特征提取网络并将PANet由三个特征层简化为一个特征层,加快了模型训练及预测的速度,同时使用CIOU对loss值进行计算使训练更加稳定。在对矿石识别完成后,利用矿石块度判断结构对预测框像素面积进行计算得到矿石的真实尺寸。在最终测试中,在将图像经过灰度化、中值滤波处理后进行测试。结果表明,相比于单独UNet图像分割算法对矿石块度进行判断,IOR方法在降低6个百分点精确率的情况下减少2.7倍模型训练时间以及提升9.07倍模型运行效率,是一种能够快速训练及预测矿石块度的方法,非常适用于传送带等需要快速识别判断的现场环境使用,同时降低了边云端计算的设备及训练成本。Aiming at the problem that the ore fragmentation of the conveyor belt needs to be manually measured,a method for detecting ore fragmentation based on deep learning is proposed.This method adopts the residual neural network structure under the Darknet framework to form the main feature extraction network of CSPDarkNet21,considering that only large pieces of ore need to be identified and judged,simple two-way feature fusion PANet is selected as the feature extraction network and PANet is simplified from three feature layers to one feature layer,it speeds up model training and prediction,and uses CIOU to calculate the loss value to make training more stable.After the ore identification is completed,the ore fragmentation judgment structure is used to calculate the pixel area of the prediction box to obtain the true size of the ore.In the final test,the test is performed after the image is processed by grayscale and median filtering.The results show that compared with the single UNet image segmentation algorithm for judging the ore size,the IOR method is reducing the accuracy by 6 percentage points.It reduces model training time by 2.7 times and improves model operation efficiency by 9.07 times.It is a method that can quickly train and predict the degree of ore crushing.It is very suitable for use in on-site environments such as conveyor belts that require rapid identification and judgment.At the same time,it reduces the cost of equipment requirements and training costs of cloud computing.

关 键 词：矿石块度 传送带 深度学习 视觉识别 

分 类 号：TD67[矿业工程—矿山机电]