Research On Bird’s Nest Detection Of Transmission Lines Based On Deep Learning

With the continuous expansion of the scale of the power grid,the impact of the bird’s nest on the transmission line is increasing year by year.As an important method to eliminate the harm of bird’s nest,there are problems such as the gradual increase of the amount of captured image data,the small target and the complex background during the inspection process.With the gradual improvement of the speed and accuracy of bird’s nest detection by machine patrol,the traditional form of manual screening of bird’s nest pictures can no longer meet the demand,so target detection technology is introduced in the patrol inspection process.Aiming at the problem of bird’s nest detection in the process of machine patrol,the target detection technology based on deep learning is used to study to improve the accuracy and speed of bird’s nest detection.Analyze the theory of object detection based on deep learning.Taking YOLOv3 network as an example,its advantages,detection methods and network structure are analyzed;through the process of YOLOv3 network processing bird’s nest pictures,the principle of target detection algorithm based on deep learning,model training and prediction process are analyzed.Build a transmission line bird’s nest detection network.The bird’s nest image dataset is constructed by rotating and adding Gaussian noise to expand the image,Mosaic method for data augmentation,and manual annotation;Construct the Ghost Net module,build the backbone network to optimize the feature layer extraction method;Improve the feature pyramid connection layer,and combine the PANet structure to construct a bottleneck network;Optimize the anchor box size,loss function form,learning rate decay form,and label vector during model training.Design experiments verify the effectiveness of the network.Firstly,the experimental hardware and software conditions are introduced,and the network evaluation indicators are analyzed;SSD network,Faster R-CNN network,and YOLO-NEST network are trained,and the loss function value,average detection accuracy,number of frames per second,and SSD network are extracted.The model size and other data are compared to verify the effectiveness of the algorithm.Design the transmission line bird’s nest detection software.Based on the Python programming language,it is written in the Pycharm software,and the software is designed using tools such as the Pyqt5 language library.The software requirements are analyzed,and the overall design scheme is proposed;the function selection module,connection module,preprocessing module,and interaction module of the software are designed;finally,the software’s image file detection,image folder detection,video file detection,and video folder detection functions are demonstrated through detection examples.Finally,it is concluded that the constructed YOLO-NEST network compresses the model size to 43.98 M,the average detection accuracy is increased to 96.11%,and the number of detection frames per second is increased to 19,which improves the accuracy and speed of bird’s nest detection;The designed detection software is simple and effective,meets engineering needs,and improves the efficiency of bird’s nest detection.