[1]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.[doi:10.11992/tis.202012023]
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Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection

References:
[1] 方苏, 李立学, 郑益慧, 等. 基于激光测距成像和图像处理的输电线路防护技术[J]. 电气自动化, 2017, 39(3):6-8, 22
FANG Su, LI Lixue, ZHENG Yihui, et al. Protection technology for transmission lines based on laser range imaging and image processing[J]. Electrical automation, 2017, 39(3):6-8, 22
[2] 王炜, 袁奇, 顾俊杰, 等. X射线无损探伤技术在检测输电线路压接金具中的应用[J]. 上海交通大学学报, 2018, 52(10):1189-1194
WANG Wei, YUAN Qi, GU Junjie, et al. Application of X-ray nondestructive flaw detection technology in transmission line’s press fittings[J]. Journal of Shanghai JiaoTong University, 2018, 52(10):1189-1194
[3] 张秋雁, 杨忠, 姜遇红, 等. 一种基于CNN的航拍输电线路图像分类方法[J]. 应用科技, 2019, 46(6):41-45
ZHANG Qiuyan, YANG Zhong, JIANG Yuhong, et al. CNN-based aerial image classification method for aerial transmission lines[J]. Applied Science and Technology, 2019, 46(6):41-45
[4] 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
[5] 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.
[6] 陈晓娟, 吴英石, 赵亮. 基于随机Hough变换的OPGW防震锤识别[J]. 黑龙江电力, 2010, 32(1):1-2, 5
CHEN Xiaojuan, WU Yingshi, ZHAO Liang. Identification of OPGW vibration damper based on random Hough transformation[J]. Heilongjiang electric power, 2010, 32(1):1-2, 5
[7] 王朝硕, 李伟性, 郑武略, 等. 一种改进SSD的输电线路电力部件识别方法[J]. 应用科技, 2020, 47(4):75-81
WANG Chaoshuo, LI Weixing, ZHENG Wulue, et al. An improved SSD method for power component identification of transmission lines[J]. Applied science and technology, 2020, 47(4):75-81
[8] 王伟, 刘国海. 绝缘子图像的边缘检测[J]. 微计算机信息, 2008, 24(27):308-309, 154
WANG Wei LIU Guohai. Image edge detection of the insulator[J]. Microcomputer information, 2008, 24(27):308-309, 154
[9] 王身丽, 黄力, 侯金华, 等. 基于最大熵的复合绝缘子表面水珠图像分割算法研究[J]. 通信电源技术, 2018, 35(1):48-50
WANG Shenli, HUANG Li, HOU Jinhua, et al. Study on segmentation method of water drops image on composite insulator based on maximum entropy[J]. Telecom power technology, 2018, 35(1):48-50
[10] 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
[11] 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
[12] 赵建坤, 王璋奇, 刘世钊. 基于灰度共生矩阵纹理特征的输电导线识别[J]. 云南电力技术, 2015, 43(2):126-129
ZHAO Jiankun, WANG Zhangqi, LIU Shizhao. Research on transmission line recognition based on GLCM texture feature[J]. Yunnan electric power, 2015, 43(2):126-129
[13] 金立军, 闫书佳, 刘源. 基于类Haar特征与级联AdaBoost算法的防震锤识别[J]. 系统仿真学报, 2012, 24(9):1806-1809
JIN Lijun, YAN Shujia, LIU Yuan. Vibration damper recognition based on Haar-like features and cascade AdaBoost classifier[J]. Journal of system simulation, 2012, 24(9):1806-1809
[14] 翟永杰, 王迪, 赵振兵. 基于目标建议与结构搜索的绝缘子识别方法[J]. 华北电力大学学报, 2016, 43(4):66-71, 78
ZHAI Yongjie, WANG Di, ZHAO Zhenbing. Recognition method of insulator based on object proposals and structure research[J]. Journal of North China Electric Power University, 2016, 43(4):66-71, 78
[15] 姜惠兰, 崔虎宝, 刘飞, 等. 基于模糊逻辑和支持向量机的高压输电线路故障分类器[J]. 中国电力, 2005, 38(3):13-17
JIANG Huilan, CUI Hubao, LIU Fei, et al. High voltage transmission line fault classification based on fuzzy logic and Support Vector Machines[J]. Electric power, 2005, 38(3):13-17
[16] 谢小瑜, 周俊煌, 张勇军. 深度学习在泛在电力物联网中的应用与挑战[J]. 电力自动化设备, 2020, 40(4):77-87
XIE Xiaoyu, ZHOU Junhuang, ZHANG Yongjun. Application and challenge of deep learning in Ubiquitous Power Internet of Things[J]. Electric power automation equipment, 2020, 40(4):77-87
[17] 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.
[18] 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
[19] 吉志朋, 张国伟, 卢秋红. 基于感受野模块的绝缘子实时识别定位方法[J]. 电工电气, 2020(9):19-22, 32
JI Zhipeng, ZHANG Guowei, LU Qiuhong. Real time detection of insulator by RFB[J]. Electrotechnics electric, 2020(9):19-22, 32
[20] 李伟性, 郑武略, 王宁, 等. 基于SSD算法的输电线路上绝缘子缺陷检测方法研究[J]. 仪器仪表用户, 2019, 26(8):1-4
LI Weixing, ZHENG Wulue, WANG Ning, et al. Research on detection method of insulator defects on transmission lines based on SSD algorithm[J]. Instrumentation customer, 2019, 26(8):1-4
[21] 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.
[22] 宣冬梅, 王菊韵, 于华, 等. 深度学习中先验知识的应用[J]. 计算机工程与设计, 2015, 36(11):3087-3091
XUAN Dongmei, WANG Juyun, YU Hua, et al. Application of prior knowledge in deep learning[J]. Computer engineering and design, 2015, 36(11):3087-3091
[23] 宋万潼, 李冰锋, 费树岷. 基于先验知识的航拍绝缘子检测方法研究[J]. 计算机工程, 2020
SONG Wantong, LI Bingfeng, FEI Shumin. Research on detection method of insulator in aerial image based on prior knowledge[J]. Computer engineering, 2020
[24] GALLEGUILLOS C, RABINOVICH A, BELONGIE S. Object categorization using co-occurrence, location and appearance[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA, 2008:1-8.
[25] 宋万潼, 李冰锋, 费树岷. 基于先验知识的航拍绝缘子检测方法研究[J/OL]. 计算机工程:1-11[2021-05-11].https://doi.org/10.19678/j.issn.1000-3428.0058448.
SONG Wantong, LI Bingfeng, FEI Shumin. Research on detection method of insulator in aerial image based on prior knowledge[J/OL]. Computer engineering, 2020:1-11[2021-05-11].https://doi.org/10.19678/j.issn.1000-3428.0058448.
[26] JIANG Chenhan, XU Hang, LIANG Xiaodan, et al. Hybrid knowledge routed modules for large-scale object detection[C]//Proceedings of the 32nd Conference on Neural Information Processing Systems. Montréal, Canada, 2018:1552-1563.
[27] 马巧慧. 基于伞裙形态学的绝缘子故障检测方法[J]. 科学技术创新, 2020(31):20-21
MA Qiaohui. An insulator fault detection method based on skirt morphology[J]. Scientific and technological innovation, 2020(31):20-21
[28] REN S, HE K, GIRSHICK R. Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of the Advances in Neural Information Processing Systems. 2015:91-99.
[29] LIU Wei, ANGUELOV D, ERHAN D. SSD:Single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016:21-37.
[30] Ren S, He K, 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, 2016, 39(6):1137-1149.
[31] 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, OH, USA, 2014:580-587.
[32] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile, 2015:1440-1448.
[33] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 42(2):318-327.
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