YOLOv2在煤岩智能识别与定位中的应用研究  

Application of YOLOv2 in intelligent recognition and location of coal and rock

作　　者：张斌[1] 苏学贵[1,2] 段振雄 常立宗 王福周 ZHANG Bin ;SU Xuegui;DUAN Zhenxiong ;CHANG Lizong ;WANG Fuzhou(School of Mining Engineering,Taiyuan University of Technology,Taiyuan 030024,China;State Key Laboratory for Geomechanics&Deep Underground Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China)

机构地区：[1]太原理工大学矿业工程学院,山西太原030024 [2]中国矿业大学(北京)深部岩土力学与地下工程国家重点实验室,北京100083

出　　处：《采矿与岩层控制工程学报》2020年第2期90-97,共8页Journal of Mining and Strata Control Engineering

基　　金：国家重点实验室开放基金资助项目(SKLGDUEK1820)。

摘　　要：综采工作面煤层走向复杂,采用"一刀切"的开采方法会加速滚轮截齿的磨损,同时开采效率也大幅降低,对煤岩的精准识别是解决此类问题、实现智能开采的关键.将基于回归方程的深度学习目标检测算法YOLOv2与线性成像模型相结合并通过该算法对井下采集煤岩图像进行了智能识别与定位,同时与Faster R-CNN,SSD对煤岩图像的识别结果进行了对比.结果显示,YOLOv2对煤岩的识别精度达到了78%,检测速度达到了63 frame/s,与Faster R-CNN,SSD相比精度高出7.7%,4.7%,而检测速度高出763%,40%;在矿井测量坐标系中YOLOv2标定的煤层边界框角点的计算坐标与实测坐标相比相对误差在3.0%~4.5%之间,相对误差较小,不会对采煤效率产生影响.研究结果表明,YOLOv2可以对煤岩进行准确快速的识别.The coal seam of fully mechanized mining face has a complex trend.The"one-size-fits-all"mining method will accelerate the wear of roller pick but reduce the mining efficiency.Accurate identification of coal and rock is the key to solve such problems and realize intelligent mining.In this paper,the deep learning target detection algorithm YOLOv2 based on regression equation combined with the linear imaging model was used to identify and locate the coal and rock images collected underground.Meanwhile,the recognition results of coal and rock images were compared with those of Faster R-CNN and SSD.The results showed that the recognition accuracy of YOLOv2 for coal and rock is around 78%,the detection speed reaches 63 frame/s,its accuracy is 7.7% and 4.7% higher than Faster R-CNN and SSD.Also the detection speed is 763% and 40% faster than those two algorithms.The relative error between the calculated coordinates of the boundary corner points of coal seam calibrated by YOLOv2 in the Mine Survey Coordinate system and the measured coordinates is between 3.0%and 4.5%.Since the relative error is small,the mining efficiency will not be affected.The results show that YOLOv2 can recognize coal and rock accurately and quickly.

关 键 词：煤岩识别 智能开采 深度学习 YOLOv2 

分 类 号：TD651[矿业工程—矿山机电]