煤矿安全2019，Vol.50Issue(12)：127-130,4.DOI:10.13347/j.cnki.mkaq.2019.12.029
基于改进FPN的输送带异物识别方法
Foreign Body Identification of Belt Based on Improved FPN
摘要
Abstract
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.关键词
带式输送机/目标检测/特征金字塔/FPN/Faster-RCNN Key words: belt conveyor/ object detection/ feature pyramid/ FPN/ Faster-RCNN分类
矿业与冶金引用本文复制引用
吴守鹏,丁恩杰,俞啸..基于改进FPN的输送带异物识别方法[J].煤矿安全,2019,50(12):127-130,4.基金项目
基 金 项 目:"十 三 五"国 家 重 点 研 发 计 划 资 助 项 目(2017YFC0804400) （2017YFC0804400）
