[1] 孙康.空间机械臂抓捕非合作目标的辨识、规划及控制策略研究[D].哈尔滨:哈尔滨工业大学, 2020. SUN K.Research on identification, kinematic planning and control strategy in capture of non-cooperative target by space manipulator[D].Harbin:Harbin Institute of Technology, 2020.(in Chinese) [2] 梁斌, 杜晓东, 李成, 等.空间机器人非合作航天器在轨服务研究进展[J].机器人, 2012, 34(2):242-256. LIANG B, DU X D, LI C, et al.Advances in space robot on-orbit servicing for non-cooperative spacecraft[J].Robot, 2012, 34(2):242-256.(in Chinese) [3] KRIZHEVSKY A, SUTSKEVER I, HINTON G E.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM, 2017, 60(6):84-90. [4] GIRSHICK R, DONAHUE J, DARRELL T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2014:580-587. [5] REN S Q, HE K M, GIRSHICK R, et al.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. [6] HE K M, GKIOXARI G, DOLLÁR P, et al.Mask R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2017:2980-2988. [7] REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once:unified, real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:779-788. [8] LIU W, ANGUELOV D, ERHAN D, et al.SSD:single shot multibox detector[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2016:21-37. [9] 刘帅.空间机器人抓捕非合作目标的智能控制与识别[D].大连:大连理工大学, 2019. LIU S.Intelligent control and recognition of space robot capturing non-cooperative targets[D].Dalian:Dalian University of Technology, 2019.(in Chinese) [10] LI Y W, BO Y M, ZHAO G P.Survey of measurement of position and pose for space non-cooperative target[C]//Proceedings of the 34th Chinese Control Conference.Washington D.C., USA:IEEE Press, 2015:5101-5106. [11] 余玉琴, 魏国亮, 王永雄.基于改进YOLOv2的无标定3D机械臂自主抓取方法[J].计算机应用研究, 2020, 37(5):1450-1455. YU Y Q, WEI G L, WANG Y X.3D uncalibrated robotic grasping method based on improved YOLOv2[J].Application Research of Computers, 2020, 37(5):1450-1455.(in Chinese) [12] REDMON J, FARHADI A.YOLO9000:better, faster, stronger[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:6517-6525. [13] REDMON J, FARHADI A.YOLOv3:an incremental improvement[EB/OL].[2021-01-02].https://arxiv.org/abs/1804.02767. [14] BOCHKOVSKIY A, WANG C Y, LIAO H Y M.YOLOv4:optimal speed and accuracy of object detection[EB/OL].[2021-01-02].https://arxiv.org/abs/2004.10934. [15] JOCHER G, STOKEN A, BOROVEC J.Ultralytics yolov5:v3.0(Versionv3.0).[EB/OL].[2021-01-02].https://github.com/ultralytics/yolov5/releases/tag/v3.0. [16] ZHANG S, WU Y X, MEN C G, et al.Tiny YOLO optimization oriented bus passenger object detection[J].Chinese Journal of Electronics, 2020, 29(1):132-138. [17] PURKAIT P, ZHAO C, ZACH C.SPP-net:deep absolute pose regression with synthetic views[EB/OL].[2021-01-02].https://arxiv.org/abs/1712.03452. [18] WANG C Y, MARK LIAO H Y, WU Y H, et al.CSPNet:a new backbone that can enhance learning capability of CNN[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:1571-1580. [19] LIU S, QI L, QIN H F, et al.Path aggregation network for instance segmentation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:8759-8768. [20] HAN K, WANG Y H, TIAN Q, et al.GhostNet:more features from cheap operations[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:1580-1589. [21] WANG Q L, WU B G, ZHU P F, et al.ECA-net:efficient channel attention for deep convolutional neural networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:11531-11539. [22] ZHENG Z H, WANG P, LIU W, et al.Distance-IoU loss:faster and better learning for bounding box regression[J].Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7):12993-13000. [23] ZHANG Z Y.Flexible camera calibration by viewing a plane from unknown orientations[C]//Proceedings of the 7th IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 1999:666-673. [24] TSAI R.A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J].IEEE Journal on Robotics and Automation, 1987, 3(4):323-344. |