[1] MIAO X R, LIU X Y, CHEN J, et al. Insulator detection in aerial images for transmission line inspection using single shot multibox detector[J]. IEEE Access, 2019,7:9945-9956.
[2] ANGELIKA W, ANNA T. Methods of detection of power transmission lines components using image analysis[J]. International Journal of Materials and Product Technology, 2015,51(3):296-309.
[3] ZHANG Y, HUANG X B, JIA J Y, et al. A recognition technology of transmission lines conductor break and surface damage based on aerial image[J]. IEEE Access, 2019,7:59022-59036.
[4] 李小薪,梁荣华. 有遮挡人脸识别综述:从子空间回归到深度学习[J]. 计算机学报, 2018,41(1):177-207.
[5] 肖德贵,辛晨,张婷,等. 显著性纹理结构特征及车载环境下的行人检测[J]. 软件学报, 2014,25(3):675-689.
[6] 张冬明,靳国庆,代锋,等. 基于深度融合的显著性目标检测算法[J]. 计算机学报, 2019,42(9):2076-2086.
[7] 朱敏超,冯涛,张钰. 基于FD-SSD的遥感图像多目标检测方法[J]. 计算机应用与软件, 2019,36(1):232-238.
[8] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Conference on Computer Vision and Pattern Recognition. 2014:580-587.
[9] GIRSHICK R. Fast R-CNN[C]// International Conference on Computer Vision. 2015:1440-1448.
[10]REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]// Advances in Neural Information Processing Systems. 2015:91-99.
[11]CAI Z W, VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2018:6154-6162.
[12]HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778.
[13]HE K M, GEORGIA G, PIOTR D, et al. Mask R-CNN[C]// Proceedings of IEEE International Conference on Computer Vision. 2017:2961-2969.
[14]REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2016:779-788.
[15]JOSEPH R, ALI F. YOLO9000: Better, faster, stronger[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2017:6517-6525.
[16]DONG X, FENG S, ZE L, et al. A target detection model based on improved Tiny-Yolov3 under the environment of mining truck[J]. IEEE Access, 2019,7:123757-123764.
[17]SUN K, XIAO B, LIU D, et al. Deep high-resolution representation learning for human pose estimation[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2019:1-12.
[18]LIN T Y, PIOTR D, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2017:936-944.
[19]LIN T Y, MICHAEL M, SERGE B, et al. Microsoft COCO: Common objects in context[C]// European Conference on Computer Vision. 2014:740-755
[20]EVERINGHAM M, ESLAMI S M A, VAN G L, et al. The pascal visual object classes challenge: A retrospective[J]. International Journal of Computer Vision, 2015,111(1):98-136.
[21]GU J Y, HU H, WANG L W, et al. Learning region features for object detection[C]// Proceedings of European Conference on Computer Vision. 2018:381-395.
[22]SHRIVASTAVA A, GUPTA A, GIRSHICK R. Training region-based object detectors with online hard example mining[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016:761-769.
[23]LIN T Y, GOYAL P, GIRSHICK R, et al. Focal Loss for dense object detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017,42(2):318-327.
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