摘要
针对大块矸石或铁器等进入运煤输送带系统易造成输送带损伤、撕裂等问题,提出了一种Faster-RCNN+双向特征金字塔网络(Double-sided Feature Pyramid Networks,DSFPN)的运煤输送带异物识别模型,模型以深度学习目标检测框架Faster-RCNN为基础,对FPN结构改进提出了DSFPN,DSFPN通过自底向上和自顶向下2个多尺度特征融合过程来解决输送带异物的多尺度问题。测试结果表明,DSFPN可以有效的提升小块矸石等小尺寸异物的检测能力,并且提升了类似锚杆、大矸石等大尺寸异物的识别精度。
Aiming at the problems of belt damage and tear caused by large-scale gangues or irons entering the coal belt system,a kind of Faster-RCNN+double-sided feature pyramid networks(DSFPN)coal-transport belt foreign object recognition model is proposed.Based on the deep learning target detection framework Faster-RCNN,the model proposes DSFPN for the improvement of FPN structure.DSFPN solves the multi-scale problem of belt foreign objects through the bottom-up and top-down multi-scale feature fusion process.The test results show that the DSFPN proposed in this paper can effectively improve the detection ability of small-sized foreign bodies such as small pieces of gangues,and improve the recognition accuracy of large-sized foreign objects such as bolts and large gangues.
作者
吴守鹏
丁恩杰
俞啸
WU Shoupeng;DING Enjie;YU Xiao(IOT Perception Mine Research Center,China University of Mining and Technology,Xuzhou 221008,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221008,China;School of Medicine Information,Xuzhou Medical University,Xuzhou 221009,China)
出处
《煤矿安全》
CAS
北大核心
2019年第12期127-130,共4页
Safety in Coal Mines
基金
“十三五”国家重点研发计划资助项目（2017YFC0804400）
