[1]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.[doi:10.11992/tis.201907023]
 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.[doi:10.11992/tis.201907023]
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注意力机制和Faster RCNN相结合的绝缘子识别

参考文献/References:
[1] 张倩, 王建平, 李帷韬. 基于反馈机制的卷积神经网络绝缘子状态检测方法[J]. 电工技术学报, 2019, 34(16): 3311–3321
ZHANG Qian, WANG Jianping, LI Weitao. Insulator state detection of convolutional neural networks based on feedback mechanism[J]. Transactions of China electrotechnical society, 2019, 34(16): 3311–3321
[2] 刘召, 张黎明, 耿美晓, 等. 基于改进的Faster R-CNN高压线缆目标检测方法[J]. 智能系统学报, 2019, 14(4): 627–634
LIU Zhao, ZHANG Liming, GENG Meixiao, et al. Object detection of high-voltage cable based on improved Faster R-CNN[J]. CAAI transactions on intelligent systems, 2019, 14(4): 627–634
[3] 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
[4] IRUANSI U, TAPAMO J R, DAVIDSON I E. An active contour approach to insulator segmentation[C]//AFRICON 2015. Addis Ababa, Ethiopia, 2015: 1–5.
[5] 左国玉, 马蕾, 徐长福, 等. 基于跨连接卷积神经网络的绝缘子检测方法[J]. 电力系统自动化, 2019, 43(4): 101–108
ZUO Guoyu, MA Lei, XU Changfu, et al. Insulator detection method based on cross-connected convolutional neural network[J]. Automation of electric power systems, 2019, 43(4): 101–108
[6] 彭向阳, 刘洋, 王柯, 等. 利用卷积神经网络进行绝缘子自动定位[J]. 武汉大学学报(信息科学版), 2019, 44(4): 563–569
PENG Xiangyang, LIU Yang, WANG Ke, et al. An automatically locating method for insulator object based on CNNs[J]. Geomatics and Information Science of Wuhan University, 2019, 44(4): 563–569
[7] 赵振兵, 崔雅萍, 戚银城, 等. 基于改进的R-FCN航拍巡线图像中的绝缘子检测方法[J]. 计算机科学, 2019, 46(3): 159–163
ZHAO Zhenbing, CUI Yaping, QI Yincheng, et al. Detection method of insulator in aerial inspection image based on modified R-FCN[J]. Computer science, 2019, 46(3): 159–163
[8] 程海燕, 翟永杰, 陈瑞. 基于Faster R-CNN的航拍图像中绝缘子识别[J]. 现代电子技术, 2019, 42(2): 98–102
CHENG Haiyan, ZHAI Yongjie, CHEN Rui. Faster R-CNN based recognition of insulators in aerial images[J]. Modern electronics technique, 2019, 42(2): 98–102
[9] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 7132–7141.
[10] 徐诚极, 王晓峰, 杨亚东. Attention-YOLO: 引入注意力机制的YOLO检测算法[J]. 计算机工程与应用, 2019, 55(6): 13–23
XU Chengji, WANG Xiaofeng, YANG Yadong. Attention-YOLO: YOLO detection algorithm that introduces attention mechanism[J]. Computer engineering and applications, 2019, 55(6): 13–23
[11] 陈庆, 闫斌, 叶润, 等. 航拍绝缘子卷积神经网络检测及自爆识别研究[J]. 电子测量与仪器学报, 2017, 31(6): 942–953
CHEN Qing, YAN Bin, YE Run, et al. Insulator detection and recognition of explosion fault based on convolutional neural networks[J]. Journal of electronic measurement and instrumentation, 2017, 31(6): 942–953
[12] RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International journal of computer vision, 2014, 115(3):211–252.
[13] 林刚, 王波, 彭辉, 等. 基于改进Faster-RCNN的输电线巡检图像多目标检测及定位[J]. 电力自动化设备, 2019, 39(5): 213–218
LIN Gang, WANG Bo, PENG Hui, et al. Multi-target detection and location of transmission line inspection image based on improved Faster-RCNN[J]. Electric power automation equipment, 2019, 39(5): 213–218
[14] 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.
[15] YANG Jianwei, LU Jiasen, LEE S, et al. Graph R-CNN for scene graph generation[C]//Proceedings of 15th European Conference on Computer Vision. Munich, Germany, 2018: 670–685.
[16] TAO Xian, ZHANG Dapeng, WANG Zihao, et al. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks[J]. IEEE transactions on systems, man, and cybernetics: systems, 2020, 50(4): 1486–1498.

备注/Memo

收稿日期:2019-07-15。
基金项目:国家自然科学基金项目(61871182,61773160)
作者简介:赵文清,教授,博士,主要研究方向为人工智能与数据挖掘。发表学术论文50余篇;程幸福,硕士研究生,主要研究方向为机器学习、深度学习、目标检测;赵振兵,副教授,博士,主要研究方向为深度学习、计算机视觉
通讯作者:赵文清.E-mail:jbzwq@126.com

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