[1]刘召,张黎明,耿美晓,等.基于改进的Faster R-CNN高压线缆目标检测方法[J].智能系统学报,2019,14(4):627-634.[doi:10.11992/tis.201905026]
 LIU Zhao,ZHANG Liming,GENG Meixiao,et al.Object detection of high-voltage cable based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2019,14(4):627-634.[doi:10.11992/tis.201905026]
点击复制

基于改进的Faster R-CNN高压线缆目标检测方法

参考文献/References:
[1] 赵玉良, 戚晖, 陈凡明, 等. 高压带电作业机器人专用遥控剥皮器的研制[J]. 微计算机信息, 2010, 26(32):146-147, 119 ZHAO Yuliang, QI Hui, CHEN Fanming, et al. Design on the remote controlled electric-driving remover for live working robot[J]. Microcomputer information, 2010, 26(32):146-147, 119
[2] 王振利, 鲁守银, 李健, 等. 高压带电作业机器人视觉伺服系统[J]. 制造业自动化, 2013, 35(7):69-72 WANG Zhenli, LU Shouyin, LI Jian, et al. Vision servo system for high-voltage live working robot[J]. Manufacturing automation, 2013, 35(7):69-72
[3] 于进勇, 丁鹏程, 王超. 卷积神经网络在目标检测中的应用综述[J]. 计算机科学, 2018, 45(S2):17-26 YU Jinyong, DING Pengcheng, WANG Chao. Overview:application of convolution neural network in object detection[J]. Computer science, 2018, 45(S2):17-26
[4] CAI Zhaowei, VASCONCELOS N. Cascade R-CNN:delving into high quality object detection[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:6154?6162.
[5] 宋焕生, 张向清, 郑宝峰, 等. 基于深度学习方法的复杂场景下车辆目标检测[J]. 计算机应用研究, 2018, 35(4):1270-1273 SONG Huansheng, ZHANG Xiangqing, ZHENG Baofeng, et al. Vehicle detection based on deep learning in complex scene[J]. Application research of computers, 2018, 35(4):1270-1273
[6] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7):1527-1554.
[7] 毕晓君, 冯雪赟. 基于改进深度学习模型C-GRBM的人体行为识别[J]. 哈尔滨工程大学学报, 2018, 39(1):156-162 BI Xiaojun, FENG Xueyun. Human action recognition based on improved depth learning model C-GRBM[J]. Journal of Harbin Engineering University, 2018, 39(1):156-162
[8] 龙慧, 朱定局, 田娟. 深度学习在智能机器人中的应用研究综述[J]. 计算机科学, 2018, 45(S2):43-47, 52 LONG Hui, ZHU Dingju, TIAN Juan. Research on deep learning used in intelligent robots[J]. Computer science, 2018, 45(S2):43-47, 52
[9] 张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望[J]. 自动化学报, 2017, 43(8):1289-1305 ZHANG Hui, WANG Kunfeng, WANG Feiyue. Advances and perspectives on applications of deep learning in visual object detection[J]. Acta automatica sinica, 2017, 43(8):1289-1305
[10] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014:580?587.
[11] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9):1904-1916.
[12] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015:1440?1448.
[13] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:779?788.
[14] 莫宏伟, 汪海波. 基于Faster R-CNN的人体行为检测研究[J]. 智能系统学报, 2018, 13(6):967-973 MO Hongwei, WANG Haibo. Research on human behavior detection based on Faster R-CNN[J]. CAAI transactions on intelligent systems, 2018, 13(6):967-973
[15] 曹宇剑, 徐国明, 史国川. 基于旋转不变Faster R-CNN的低空装甲目标检测[J]. 激光与光电子学进展, 2018, 55(10):101501 CAO Yujian, XU Guoming, SHI Guochuan. Low altitude armored target detection based on rotation invariant faster R-CNN[J]. Laser and optoelectronics progress, 2018, 55(10):101501
[16] 魏湧明, 全吉成, 侯宇青阳. 基于YOLO v2的无人机航拍图像定位研究[J]. 激光与光电子学进展, 2017, 54(11):111002 WEI Yongming, QUAN Jicheng, HOU Yuqingyang. Aerial image location of unmanned aerial vehicle based on YOLO v2[J]. Laser and optoelectronics progress, 2017, 54(11):111002
[17] REN Shaoqing, HE Kaiming, GIRSHICK R, SUN J. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6):1137-1149.
[18] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016:770?778.
[19] XIE Saining, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:5987?5995.
相似文献/References:
[1]胡光龙,秦世引.动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J].智能系统学报,2012,7(1):61.
 HU Guanglong,QIN Shiyin.High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J].CAAI Transactions on Intelligent Systems,2012,7():61.
[2]韩峥,刘华平,黄文炳,等.基于Kinect的机械臂目标抓取[J].智能系统学报,2013,8(2):149.[doi:10.3969/j.issn.1673-4785.201212038]
 HAN Zheng,LIU Huaping,HUANG Wenbing,et al.Kinect-based object grasping by manipulator[J].CAAI Transactions on Intelligent Systems,2013,8():149.[doi:10.3969/j.issn.1673-4785.201212038]
[3]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(2):193.[doi:10.3969/j.issn.1673-4785.201405060]
 ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10():193.[doi:10.3969/j.issn.1673-4785.201405060]
[4]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(1):1.[doi:10.3969/j.issn.1673-4785.201403072]
 DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10():1.[doi:10.3969/j.issn.1673-4785.201403072]
[5]韩延彬,郭晓鹏,魏延文,等.RGB和HSI颜色空间的一种改进的阴影消除算法[J].智能系统学报,2015,10(5):769.[doi:10.11992/tis.201410010]
 HAN Yanbin,GUO Xiaopeng,WEI Yanwen,et al.An improved shadow removal algorithm based on RGB and HSI color spaces[J].CAAI Transactions on Intelligent Systems,2015,10():769.[doi:10.11992/tis.201410010]
[6]曾宪华,易荣辉,何姗姗.流形排序的交互式图像分割[J].智能系统学报,2016,11(1):117.[doi:10.11992/tis.201505037]
 ZENG Xianhua,YI Ronghui,HE Shanshan.Interactive image segmentation based on manifold ranking[J].CAAI Transactions on Intelligent Systems,2016,11():117.[doi:10.11992/tis.201505037]
[7]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[8]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
 LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11():567.[doi:10.11992/tis.201511028]
[9]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
 MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11():728.[doi:10.11992/tis.201611021]
[10]王亚杰,邱虹坤,吴燕燕,等.计算机博弈的研究与发展[J].智能系统学报,2016,11(6):788.[doi:10.11992/tis.201609006]
 WANG Yajie,QIU Hongkun,WU Yanyan,et al.Research and development of computer games[J].CAAI Transactions on Intelligent Systems,2016,11():788.[doi:10.11992/tis.201609006]
[11]葛园园,许有疆,赵帅,等.自动驾驶场景下小且密集的交通标志检测[J].智能系统学报,2018,13(3):366.[doi:10.11992/tis.201706040]
 GE Yuanyuan,XU Youjiang,ZHAO Shuai,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13():366.[doi:10.11992/tis.201706040]
[12]莫宏伟,汪海波.基于Faster R-CNN的人体行为检测研究[J].智能系统学报,2018,13(6):967.[doi:10.11992/tis.201801025]
 MO Hongwei,WANG Haibo.Research on human behavior detection based on Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2018,13():967.[doi:10.11992/tis.201801025]
[13]单义,杨金福,武随烁,等.基于跳跃连接金字塔模型的小目标检测[J].智能系统学报,2019,14(6):1144.[doi:10.11992/tis.201905041]
 SHAN Yi,YANG Jinfu,WU Suishuo,et al.Skip feature pyramid network with a global receptive field for small object detection[J].CAAI Transactions on Intelligent Systems,2019,14():1144.[doi:10.11992/tis.201905041]
[14]赵振兵,江爱雪,戚银城,等.嵌入遮挡关系模块的SSD模型的输电线路图像金具检测[J].智能系统学报,2020,15(4):656.[doi:10.11992/tis.202001008]
 ZHAO Zhenbing,JIANG Aixue,QI Yincheng,et al.Fittings detection in transmission line images with SSD model embedded occlusion relation module[J].CAAI Transactions on Intelligent Systems,2020,15():656.[doi:10.11992/tis.202001008]
[15]张新钰,邹镇洪,李志伟,等.面向自动驾驶目标检测的深度多模态融合技术[J].智能系统学报,2020,15(4):758.[doi:10.11992/tis.202002010]
 ZHANG Xinyu,ZOU Zhenhong,LI Zhiwei,et al.Deep multi-modal fusion in object detection for autonomous driving[J].CAAI Transactions on Intelligent Systems,2020,15():758.[doi:10.11992/tis.202002010]
[16]王照国,张红云,苗夺谦.基于F1值的非极大值抑制阈值自动选取方法[J].智能系统学报,2020,15(5):1006.[doi:10.11992/tis.202006056]
 WANG Zhaoguo,ZHANG Hongyun,MIAO Duoqian.Automatic selection method of non-maximum suppression threshold based on F1 score[J].CAAI Transactions on Intelligent Systems,2020,15():1006.[doi:10.11992/tis.202006056]
[17]伍锡如,凌星雨.基于改进的Faster RCNN面部表情检测算法[J].智能系统学报,2021,16(2):210.[doi:10.11992/tis.201910020]
 WU Xiru,LING Xingyu.Facial expression recognition based on improved Faster RCNN[J].CAAI Transactions on Intelligent Systems,2021,16():210.[doi:10.11992/tis.201910020]
[18]翟永杰,杨旭,赵振兵,等.融合共现推理的Faster R-CNN输电线路金具检测[J].智能系统学报,2021,16(2):237.[doi:10.11992/tis.202012023]
 ZHAI Yongjie,YANG Xu,ZHAO Zhenbing,et al.Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J].CAAI Transactions on Intelligent Systems,2021,16():237.[doi:10.11992/tis.202012023]
[19]洪恺临,曹江涛,姬晓飞.改进Center-Net网络的自主喷涂机器人室内窗户检测[J].智能系统学报,2021,16(3):425.[doi:10.11992/tis.202005016]
 HONG Kailin,CAO Jiangtao,JI Xiaofei.Indoor window detection of autonomous spraying robot based on improved CenterNet network[J].CAAI Transactions on Intelligent Systems,2021,16():425.[doi:10.11992/tis.202005016]
[20]朱齐丹,李小铜,郑天昊.舰载机位姿实时视觉测量算法研究[J].智能系统学报,2021,16(6):1045.[doi:10.11992/tis.202103014]
 ZHU Qidan,LI Xiaotong,ZHENG Tianhao.Research on real-time vision measurement algorithm of shipborne aircraft pose[J].CAAI Transactions on Intelligent Systems,2021,16():1045.[doi:10.11992/tis.202103014]

备注/Memo

收稿日期:2019-05-14。
基金项目:天津市智能制造科技重大专项(17ZXZNGX00120).
作者简介:刘召,男,1979年生,博士,主要研究方向为机器人及其自动化装备。作为项目负责人和技术骨干完成研究课题10余项,其中,863项目1项,985项目1项,国防科技课题4项,日本学术振兴会科学研究补助金基础研究项目1项。发表学术论文10余篇;张黎明,男,1969年生,高级技师,主要研究方向为智能配电网。个人获24项国家专利,带领团队为公司实现技术革新400多项,获国家专利158项,20余项填补智电网建设空白;耿美晓,女,1990年生,硕士,主要研究方向为图像处理、机器人学习与人工智能。
通讯作者:耿美晓.E-mail:lin@thtcrobot.com

更新日期/Last Update: 2019-08-25
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134