Bird’s Nest Detection Algorithm for Transmission Lines Based on Deep Learning

The bird’s nest on the power transmission tower may cause the occurrence of bird flash accidents and threaten the safe and reliable operation of the power grid. The realization of the independent identification and positioning of the bird’s nest has always been a research hotspot. With the gradual deepening of the application of UAV inspection, higher requirements are put forward for the accuracy and speed of the bird’s nest recognition algorithm. This paper proposes a bird’s nest defect recognition method based on YOLOv5, which is composed of backbone network, FPN and YOLO head. After multiple rounds of training on the construction of the bird’s nest defect database and model of the transmission line, the independent identification and positioning of the bird’s nest has been realized. The results show that the recognition rate of the YOLOv5 model for the bird’s nest can reach 83.4%, and the FPS can reach 85.32. The recognition algorithm based on YOLOv5 proposed in this paper can meet the real-time and accuracy requirements of UAV inspection for target detection.