[1]戚银城,赵席彬,耿劭锋,等.基于遮挡关系推理的输电线路图像金具检测[J].智能系统学报,2022,17(6):1154-1162.[doi:10.11992/tis.202108036]
 QI Yincheng,ZHAO Xibin,GENG Shaofeng,et al.Fittings detection in transmission line images with occlusion relation inference[J].CAAI Transactions on Intelligent Systems,2022,17(6):1154-1162.[doi:10.11992/tis.202108036]
点击复制

基于遮挡关系推理的输电线路图像金具检测

参考文献/References:
[1] 赵强, 左石. 输电线路金具理论与应用[M]. 北京: 中国电力出版社, 2013: 2-12.
[2] 苏奕辉, 梁伟放. 架空输电线路隐患、缺陷及故障表象辨识图册[M]. 北京: 中国电力出版社, 2017.
[3] DENG Chuang, WANG Shengwei, HUANG Zhi, et al. Unmanned aerial vehicles for power line inspection: a cooperative way in platforms and communications[J]. Journal of communications, 2014, 9(9): 687–692.
[4] TONG Weiguo, YUAN Jinsha, LI Baoshu. Application of image processing in patrol inspection of overhead transmission line by helicopter[J]. Power system technology, 2010, 34(12): 204–208.
[5] 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
[6] 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
[7] YAN Shujia, JIN Lijun, DUAN Shaohui, et al. Power line image segmentation and extra matter recognition based on improved Otsu algorithm[C]//2nd International Conference on Electric Power Equipment-Switching Technology. Shenzhen: China, 2013: 1-4.
[8] 宋伟, 左丹, 邓邦飞, 等. 高压输电线防震锤锈蚀缺陷检测[J]. 仪器仪表学报, 2016, 37(S1): 113–117
SONG Wei, ZUO Dan, DENG Bangfei, et al. Corrosion defect detection of earthquake hammer for high voltage transmission line[J]. Chinese journal of scientific instrument, 2016, 37(S1): 113–117
[9] 杨罡, 孙昌雯, 张娜, 等. 基于多尺度特征融合的输电线路关键部件检测[J]. 电测与仪表, 2020, 57(3): 54–59
YANG Gang, SUN Changwen, ZHANG Na, et al. Detection of key components of transmission lines based on multi-scale feature fusion[J]. Electrical measurement & instrumentation, 2020, 57(3): 54–59
[10] 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): 656–662
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(4): 656–662
[11] 赵振兵, 李延旭, 甄珍, 等. 结合KL散度和形状约束的Faster R-CNN典型金具检测方法[J]. 高电压技术, 2020, 46(9): 3018–3026
ZHAO Zhenbing, LI Yanxu, ZHEN Zhen, et al. Typical fittings detection method with faster R-CNN combining KL divergence and shape constraints[J]. High voltage engineering, 2020, 46(9): 3018–3026
[12] 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.
[13] LIU Yong, WANG Ruiping, SHAN Shiguang, et al. Structure inference net: object detection using scene-level context and instance-level relationships[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018 : 6985-6994.
[14] 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(06): 60–65
TANG Yue, HAN Jun, WEI Wenli, et al. Research on component identification and defect detection in power transmission lines using deep learning[J]. Electronic measurement technology, 2018, 41(06): 60–65
[15] 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.
[16] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
[17] JIANG Borui, LUO Ruixuan, MAO Jiayuan, et al. Acquisition of localization confidence for accurate object detection[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer Cham, 2018: 816-832.
[18] XU Danfei, ZHU Yuke, CHOY C B, et al. Scene graph generation by iterative message passing[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 3097-3106.
[19] TANG Duyu, QIN Bing, LIU Ting. Document modeling with gated recurrent neural network for sentiment classification[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: ACL, 2015: 1422-1432.
[20] EVERINGHAM M, ESLAMI S M A, GOOL L, et al. The pascal visual object classes challenge: a retrospective[J]. International journal of computer vision, 2015, 111(1): 98–136.
[21] 顾晓东, 唐丹宏, 黄晓华. 基于深度学习的电网巡检图像缺陷检测与识别[J]. 电力系统保护与控制, 2021, 49(5): 91–97
GU Xiaodong, TANG Danhong, HUANG Xiaohua. Defect detection and recognition of power grid inspection images based on deep learning[J]. Power system protection and control, 2021, 49(5): 91–97
[22] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer Cham, 2016: 21-37.
[23] SONG Yifeng, WANG Lin, JIANG Yong, et al. A vision-based method for the broken spacer detection[C]//2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems. Shenyang: IEEE, 2015: 715-719.
[24] 戚银城, 武学良, 赵振兵, 等. 嵌入双注意力机制的Faster R-CNN航拍输电线路螺栓缺陷检测[J]. 中国图象图形学报, 2021, 26(11): 2594–2604
QI Yincheng, WU Xueliang, ZHAO Zhenbing, et al. Faster R-CNN aerial photography transmission line bolt defect detection with embedded dual attention mechanism[J]. Chinese journal of image and graphics, 2021, 26(11): 2594–2604
[25] WANG Hongchao, SHAO Yunfeng, ZOU Suli, et al. Detection of cotter pins missing of connection fittings on transmission lines of power system[C]//2021 40th Chinese Control Conference. Shanghai: IEEE, 2021: 6873-6879.
相似文献/References:
[1]岳湘,王洪光,姜勇,等.一种110kV输电线路巡检机器人机构研究[J].智能系统学报,2016,11(2):155.[doi:10.11992/tis.201511032]
 YUE Xiang,WANG Hongguang,JIANG Yong,et al.Design approach for a 110 kV power transmission line inspection robot mechanism[J].CAAI Transactions on Intelligent Systems,2016,11():155.[doi:10.11992/tis.201511032]
[2]宋屹峰,王洪光,康文杰,等.DOI面向断股补修作业任务的电力机器人机构设计[J].智能系统学报,2017,12(2):150.[doi:10.11992/tis.201605004]
 SONG Yifeng,WANG Hongguang,KANG Wenjie,et al.Optimizing electric power robot design for broken-strand repair tasks[J].CAAI Transactions on Intelligent Systems,2017,12():150.[doi:10.11992/tis.201605004]
[3]赵振兵,王睿,赵文清,等.基于图知识推理的输电线路缺销螺栓识别方法[J].智能系统学报,2023,18(2):372.[doi:10.11992/tis.202205004]
 ZHAO Zhenbing,WANG Rui,ZHAO Wenqing,et al.Pin-missing bolts recognition method for transmission lines based on graph knowledge reasoning[J].CAAI Transactions on Intelligent Systems,2023,18():372.[doi:10.11992/tis.202205004]
[4]戚银城,耿劭锋,赵振兵,等.基于特征迁移的螺栓图像超分辨率处理方法[J].智能系统学报,2023,18(4):858.[doi:10.11992/tis.202201009]
 QI Yincheng,GENG Shaofeng,ZHAO Zhenbing,et al.A method for super resolution processing of bolt image based on feature transfer[J].CAAI Transactions on Intelligent Systems,2023,18():858.[doi:10.11992/tis.202201009]
[5]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]
[6]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.[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():980.[doi:10.11992/tis.202107026]
[7]王巍,杨耀权,王乾铭,等.嵌入视觉关系掩码的多类别金具检测方法[J].智能系统学报,2023,18(3):440.[doi:10.11992/tis.202202021]
 WANG Wei,YANG Yaoquan,WANG Qianming,et al.A multi-category fitting detection method with embedded visual relation masks[J].CAAI Transactions on Intelligent Systems,2023,18():440.[doi:10.11992/tis.202202021]

备注/Memo

收稿日期:2021-08-30。
基金项目:国家自然科学基金项目(61871182);河北省自然科学基金项目(F2020502009).
作者简介:戚银城,教授,主要研究方向为电力系统通信与信息处理。承担国家自然科学基金、国网福建电科院、国网山东电科院等多项课题研究。发表学术论文80余篇。;赵席彬,硕士研究生,主要研究方向为行为识别及电力目标检测。;耿劭锋,硕士研究生,主要研究方向为电力目标检测及图像超分。
通讯作者:赵振兵.E-mail:zhaozhenbing@ncepu.edu.cn

更新日期/Last Update: 1900-01-01
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 邮箱:tis@vip.sina.com