Research On Bird Detection Method Of Transmission Lines Based On Convolutional Neural Network

Electricity transmission plays a main role in the power system and is an indispensable part of the power grid system.However,the general transmission links are in the wild environment,which causes the transmission line to be affected by many factors during operation.Bird damage is one of the three major hazards which threaten the safe operation of transmission lines,causing problems such as flashing,short circuit,and tripping.At present,the power system has taken some measures to prevent bird damage:manual inspection,bird thorn prevention,driving windmills,ultrasonic waves,etc.The human and material resources are huge,but the control effect is not obvious.,and an accurate,efficient and real-time bird repellent technology is needed.This paper proposes an artificial intelligence method to detect birds,and ultrasonically drive birds according to the test results to ensure the safe and stable operation of the transmission line.This paper uses the YOLO(You Only Look Once)algorithm for bird target detection.The YOLO algorithm is an end-to-end one-stage target detection algorithm that does not detect small targets in an image.However,most of the tasks in this paper need to be detected in the image,and they belong to the category of small targets.Therefore,this paper uses YOLO model,multi-scale prediction and residual module structure to detect targets,the residual network can reduce the problem of model accuracy degradation due to the increase of the number of network layers,so the residual module is added to enhance the feature extraction ability.In this paper,the sample image is collected by the camera,the data is expanded,the data set is created by the annotation tool,the network model is built,and the model training is carried out by using the idea of migration learning.Through the experimental results,the bird detection method based on convolutional neural network proposed in this paper can achieve the purpose of bird disease prevention in real time,high efficiency and accuracy.