Foreign Object Detection of Transmission Lines Based on Faster R-CNN

被引:10
|
作者
Guo, Shuqiang [1 ]
Bai, Qianlong [1 ]
Zhou, Xinxin [1 ]
机构
[1] Northeast Elect Power Univ, Jilin 132012, Jilin, Peoples R China
来源
INFORMATION SCIENCE AND APPLICATIONS | 2020年 / 621卷
关键词
Faster R-CNN; Object detection; Transmission line;
D O I
10.1007/978-981-15-1465-4_28
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The object detection method based on RCNN network model has good mobility and robustness compared with the traditional methods. Classical foreign object detection algorithms for transmission line, such as SIFT and ORB feature matching algorithms. These methods have low recognition accuracy for edge blurred images and complex background images. In view of the above deficiencies, this paper constructs a transmission line training data set based on the characteristics of the collected transmission line images, and trains the Faster R-CNN model to detect the falling objects, kites, balloons and other foreign objects in the transmission lines. The experimental results show that compared with the traditional object recognition method, Faster R-CNN not only overcomes the instability of manual extraction features, but also improves the accuracy of foreign object detection in transmission lines. It can realize the detection of foreign objects in transmission lines in complex scenes.
引用
收藏
页码:269 / 275
页数:7
相关论文
共 50 条
  • [21] AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN
    He, Zhige
    He, Yuanqing
    IEEE ACCESS, 2025, 13 : 36050 - 36064
  • [22] Face Detection with the Faster R-CNN
    Jiang, Huaizu
    Learned-Miller, Erik
    2017 12TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2017), 2017, : 650 - 657
  • [23] Rapid Cigarette Detection Based on Faster R-CNN
    Han, Guijin
    Li, Qian
    Zhou, You
    Duan, Jiawei
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 2759 - 2765
  • [24] Traffic Signs Detection Based on Faster R-CNN
    Zuo, Zhongrong
    Yu, Kai
    Zhou, Qiao
    Wang, Xu
    Li, Ting
    2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS (ICDCSW), 2017, : 286 - 288
  • [25] Automatic detection of books based on Faster R-CNN
    Zhu, Beibei
    Wu, Xiaoyu
    Yang, Lei
    Shen, Yinghua
    Wu, Linglin
    2016 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION PROCESSING, DATA MINING, AND WIRELESS COMMUNICATIONS (DIPDMWC), 2016, : 8 - 12
  • [26] Detection Method of Insulator Based on Faster R-CNN
    Ma, Lei
    Xu, Changfu
    Zuo, Guoyu
    Bo, Bin
    Tao, Fengbo
    2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER), 2017, : 1410 - 1414
  • [27] Fabric Defect Detection Based on Faster R-CNN
    Liu, Zhoufeng
    Liu, Xianghui
    Li, Chunlei
    Li, Bicao
    Wang, Baorui
    NINTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2017), 2018, 10615
  • [28] Railway Foreign Body Intrusion Detection Based on Faster R-CNN Network Model
    Xu Y.
    Tao H.
    Hu L.
    Tiedao Xuebao/Journal of the China Railway Society, 2020, 42 (05): : 91 - 98
  • [29] Faster R-CNN: an Approach to Real-Time Object Detection
    Gavrilescu, Raducu
    Fosalau, Cristian
    Zet, Cristian
    Skoczylas, Marcin
    Cotovanu, David
    2018 INTERNATIONAL CONFERENCE AND EXPOSITION ON ELECTRICAL AND POWER ENGINEERING (EPE), 2018, : 165 - 168
  • [30] Faster R-CNN Based Microscopic Cell Detection
    Yang, Su
    Fang, Bin
    Tang, Wei
    Wu, Xuegang
    Qian, Jiye
    Yang, Weibin
    2017 INTERNATIONAL CONFERENCE ON SECURITY, PATTERN ANALYSIS, AND CYBERNETICS (SPAC), 2017, : 345 - 350