An Automatic Detection Method of Bird’s Nest on Transmission Line Tower Based on Faster_RCNN

• Published in: IEEE access Vol. 8; pp. 164214 - 164221
• Main Authors: Li, Fan, Xin, Jianbo, Chen, Tian, Xin, Lijie, Wei, Zixiang, Li, Yanglin, Zhang, Yu, Jin, Hua, Tu, Youping, Zhou, Xuguang, Liao, Haoshuang
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020; IEEE
• Subjects: Artificial neural networks; Automatic transmissions; Bird strike tests; Bird’s nest inspection; Convolution; Economic impact; Electric power grids; faster_RCNN; Identification methods; Image transmission; machine learning; Neural networks; Target detection; Technical services; transmission line; Transmission lines; Transmission towers; UAV patrol inspection; Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3022419

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.