[1]翟永杰,王乾铭,杨旭,等.融合外部知识的输电线路多金具解耦检测方法[J].智能系统学报,2022,17(5):980-989.[doi:10.11992/tis.202107026]
 ZHAI Yongjie,WANG Qianming,YANG Xu,et al.A multi-fitting decoupling detection method for transmission lines based on external knowledge[J].CAAI Transactions on Intelligent Systems,2022,17(5):980-989.[doi:10.11992/tis.202107026]
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融合外部知识的输电线路多金具解耦检测方法

参考文献/References:
[1] POURNARAS E, ESPEJO-URIBE J. Self-repairable smart grids via online coordination of smart transformers[J]. IEEE transactions on industrial informatics, 2017, 13(4): 1783–1793.
[2] SADYKOVA D, PERNEBAYEVA D, BAGHERI M, et al. IN-YOLO: real-time detection of outdoor high voltage insulators using UAV imaging[J]. IEEE transactions on power delivery, 2020, 35(3): 1599–1601.
[3] 赵文清, 程幸福, 赵振兵, 等. 注意力机制和Faster RCNN相结合的绝缘子识别[J]. 智能系统学报, 2020, 15(1): 92–98
ZHAO Wenqing, CHENG Xingfu, ZHAO Zhenbing, et al. Insulator recognition based on attention mechanism and Faster RCNN[J]. CAAI transactions on intelligent systems, 2020, 15(1): 92–98
[4] 赵振兵, 翟永杰, 张珂. 电力视觉技术[M]. 北京: 中国电力出版社, 2020.
[5] FANG Ting, JIN Xin, HU Xingliu, et al. A fast insulator-contour-detection-algorithm on power transmission lines images[J]. Applied mechanics and materials, 2012, 201/202: 337–343.
[6] 黄宵宁, 张真良. 直升机巡检航拍图像中绝缘子图像的提取算法[J]. 电网技术, 2010, 34(1): 194–197
HUANG Xiaoning, ZHANG Zhenliang. A method to extract insulator image from aerial image of helicopter patrol[J]. Power system technology, 2010, 34(1): 194–197
[7] WU Qinggang, AN Jubai. An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images[J]. IEEE transactions on geoscience and remote sensing, 2014, 52(6): 3613–3626.
[8] 金立军, 胡娟, 闫书佳. 基于图像的高压输电线间隔棒故障诊断方法[J]. 高电压技术, 2013, 39(5): 1040–1045
JIN Lijun, HU Juan, YAN Shujia. Method of spacer fault diagnose on transmission line based on image procession[J]. High voltage engineering, 2013, 39(5): 1040–1045
[9] 王万国, 张晶晶, 韩军, 等. 基于无人机图像的输电线断股与异物缺陷检测方法[J]. 计算机应用, 2015, 35(8): 2404–2408
WANG Wanguo, ZHANG Jingjing, HAN Jun, et al. Broken strand and foreign body fault detection method for power transmission line based on unmanned aerial vehicle image[J]. Journal of computer applications, 2015, 35(8): 2404–2408
[10] 胡彩石, 吴功平, 曹珩, 等. 高压输电线路巡线机器人障碍物视觉检测识别研究[J]. 传感技术学报, 2008, 21(12): 2092–2096
HU Caishi, WU Gongping, CAO Heng, et al. Research of obstacle recognition based on vision for high voltage transmission line inspection robot[J]. Chinese journal of sensors and actuators, 2008, 21(12): 2092–2096
[11] 张东, 邱翔, 曹成功, 等. 结合聚合通道和复频域特征的防震锤检测算法[J]. 计算机技术与发展, 2020, 30(3): 147–151
ZHANG Dong, QIU Xiang, CAO Chenggong, et al. An vibration damper detection algorithm combined with aggregation channel and complex frequency domain features[J]. Computer technology and development, 2020, 30(3): 147–151
[12] 付晶, 邵瑰玮, 吴亮, 等. 利用层次模型进行训练学习的线路设备缺陷检测方法[J]. 高电压技术, 2017, 43(1): 266–275
FU Jing, SHAO Guiwei, WU Liang, et al. Defect detection of line facility using hierarchical model with learning algorithm[J]. High voltage engineering, 2017, 43(1): 266–275
[13] 汤踊, 韩军, 魏文力, 等. 深度学习在输电线路中部件识别与缺陷检测的研究[J]. 电子测量技术, 2018, 41(6): 60–65
TANG Yong, HAN Jun, WEI Wenli, et al. Research on part recognition and defect detection of trainsmission line in deep learning[J]. Electronic measurement technology, 2018, 41(6): 60–65
[14] 高强, 廉启旺. 航拍图像中绝缘子目标检测的研究[J]. 电测与仪表, 2019, 56(5): 119–123
GAO Qiang, LIAN Qiwang. Research on target detection of insulator in aerial image[J]. Electrical measurement & instrumentation, 2019, 56(5): 119–123
[15] HU Han, GU Jiayuan, ZHANG Zheng, et al. Relation networks for object detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, IEEE, 2018: 3588?3597.
[16] CHEN Xinlei, GUPTA A. Spatial memory for context reasoning in object detection[C]//2017 IEEE International Conference on Computer Vision. Venice, IEEE, 2017: 4106?4116.
[17] ZENG X, OUYANG Wanli,YANG Bin, et al. Gated bi-directional cnn for object detection[C]//European conference on computer vision. Springer, Cham, 2016: 354?369.
[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): 237–246
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(2): 237–246
[19] JIANG CHENHAN, XU HANG, LIANG XIANGDAN, et al. Hybrid knowledge routed modules for large-scale object detection[EB/OL]. (2018?10?30)[2021?01?01].https://arxiv.org/abs/1810.12681.
[20] GORI M, MONFARDINI G, SCARSELLI F. A new model for learning in graph domains[C]//Proceedings of 2005 IEEE International Joint Conference on Neural Networks. Montreal, IEEE, 2005: 729?734.
[21] DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, Curran Associates Inc., 2016: 3844?3852.
[22] BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral networks and locally connected networks on graphs[EB/OL]. (2014?05?21)[2020?12?12].https://arxiv.org/abs/1312.6203.
[23] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2016?12?09)[2020?11?30].https://arxiv.org/abs/1609.02907.
[24] VELI?KOVI? P, CUCURULL G, CASANOVA A, et al. Graph attention networks[EB/OL]. (2018?02?04)[2020?10?01].https://arxiv.org/abs/1710.10903.
[25] JIANG Borui, LUO Ruixuan, MAO Jiayuan, et al. Acquisition of Localization Confidence for Accurate Object Detection[C]//European Conference on Computer Vision. Cham: Springer, 2018: 816?832.
[26] WU Yue, CHEN Yinpeng, YUAN Lu, et al. Rethinking classification and localization for object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, IEEE, 2020: 10183?10192.
[27] SONG Guanglu, LIU Yu, WANG Xiaogang. Revisiting the sibling head in object detector[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, IEEE, 2020: 11560?11569.
[28] REN Shaoqing, HE Kaiming, 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.
[29] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[M]//Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016: 21?37.
[30] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980?2988.
[31] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020?04?23)[2021?01?10].https://arxiv.org/abs/2004.10934.
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备注/Memo

收稿日期:2021-07-15。
基金项目:国家自然科学基金项目(61773160, 61871182);北京市自然科学基金项目(4192055);河北省自然科学基金项目(F2021502013, F2020502009, F2021502008).
作者简介:翟永杰,教授,博士,主要研究方向为电力视觉。主持国家自然科学基金面上项目1项、河北省自然科学基金项目1项,主持横向科研项目多项,参与国家重点研发计划项目1项,获山东省科技进步一等奖1项。授权发明专利10项,编著1部,参编教材1部、著作3部,发表学术论文30余篇;王乾铭,博士研究生,主要研究方向为电力视觉与知识推理;赵振兵,教授,博士,主要研究方向为电力视觉。主持国家自然科学基金等纵向课题10项,获省科技进步一等奖1项(第3完成人)。以第1完成人获得国家专利授权16项,以第1作者出版专著2部,发表学术论文50余篇。
通讯作者:赵振兵. E-mail:zhaozhenbing@ncepu.edu.cn

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