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

Electricity is an indispensable and important resource for the development of modern society,and it is also a public facility related to people’s livelihood and safety.Maintaining a stable supply of electricity is the key to the stable development of national and social production.In order to ensure the stable operation of transmission lines,it is necessary to monitor the nesting behavior of birds on overhead line towers.Aiming at the problem of bird nests on transmission line towers,a detection algorithm for bird nests on transmission line towers based on deep learning UAV captured images is proposed.The algorithm proposed in this study uses YOLOXs as the benchmark model to design a high-precision bird’s nest detection algorithm for images captured by drones.The specific work is reflected in:On the one hand,on the basis of YOLOXs,starting from the network structure,the feature extraction ability of the network is improved.Introduce the Ghost Module module: effectively reduce model parameters and calculation amount,optimize the feature map,and improve the detection efficiency of the model;introduce the CBAM attention mechanism: enhance the expression ability of key features in the feature map,resist chaotic information,and focus on specific targets In terms of detection;introduce the Swin Transformer Block module: capture global information and richer context information,increase the ability of the model to capture different local information and use the self-attention mechanism to explore the potential of feature representation,improve the ability of the model to extract target features,and name it For YOLOX Lite.On the other hand,on the basis of YOLOXs,starting from the target detection postprocessing algorithm,the algorithm is optimized for bird’s nest detection.A sampling method for sub-positive samples is proposed: according to the degree of matching,it is divided into the concept of positive samples and sub-positive samples,shaping the competitive relationship between samples,and improving the detector’s ability to detect dense and overlapping targets;for candidate samples Reconstruct the positive and negative weight assignment function:specify the positive and negative weights of samples from different perspectives,provide more differentiated supervision signals to distinguish important and unimportant samples,and thus produce a more efficient model;introduce arbitrary distribution modeling border regression.Taking an arbitrary distribution P(x)to model the representation of the real frame can obtain a more reliable and accurate prediction frame,and name it YOLOX Lite V2.Following YOLOX Lite’s network structure improvement strategy and YOLOX Lite V2’s detection post-processing algorithm improvement strategy,the transmission line bird’s nest detection algorithm YOLOX Lite V3 in this research is finally formed.The experimental results show that: m AP@.5 and m AP.5:.95 of the newly proposed transmission line bird’s nest detection algorithm on the Vis Drone2019 dataset are 3.9% and 2.9% higher than YOLOXs.The m AP@.5 on the self-made bird’s nest dataset is 1.2% higher than YOLOXs.The results show that the newly proposed bird’s nest detection algorithm for transmission lines has certain academic innovation and engineering application value.