An automatic detection method of bird's nest on transmission line tower based on Faster_RCNN

Fan Li*, Jianbo Xin, Tian Chen, Lijie Xin, Zixiang Wei, Yanglin Li, Yu Zhang, Hua Jin, Youping Tu, Xuguang Zhou, Haoshuang Liao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)

Abstract

The bird's nest on the transmission line tower has a bad impact on the transmission equipment, and even threaten the safe and stable operation of the power grid. In recent years, the number of bird pest in transmission line is increasing year by year, resulting in increasing economic losses. The traditional bird's nest identification method of transmission line is time-consuming and labor-intensive, and its security level is low. Therefore, this paper proposes an automatic detection method of bird's nest on transmission line tower based on Faster-RCNN convolution neural network. This method can automatically identify the location of the bird's nest on the transmission line tower by using the image collected by unmanned aerial vehicle (UAV). The problem of insufficient training samples and overfitting of neural network classifier is solved by enlarging the bird's nest image. The experimental results show that this method can effectively detect bird's nest targets in complex environment, and the highest recall rate can reach 95.38%, the highest F1 score can reach 96.87%, and the detection time of each image can reach 0.154s. Compared with the traditional nest detection method, this method has stronger applicability and generalization ability. It provides technical support for analyzing bird activities and taking effective preventive measures.

Original languageEnglish
Pages (from-to)164214-164221
Number of pages8
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Bird's nest inspection
  • Faster_RCNN
  • Machine learning
  • Transmission line
  • UAV patrol inspection

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