Image intelligent recognition technology for trigger sources of external hidden dangers in transmission lines based on convolutional neural networks
LI Guoqiang
ZHANG Feng
LIAO Ruchao
LI Duanjiao
LI Xionggang
Abstract：[Objective]As the power system continues to expand,transmission lines,being a crucial channel for power transmission,require safe and stable operation.However,transmission lines,long exposed to the complex and variable natural environment,face multiple safety risks such as external force damage and equipment aging.To enable high-precision and high-efficiency automatic detection of potential hazards in transmission lines,this study proposed an intelligent identification technology for external damage risks in transmission lines,based on deep learning.[Methods]This study developed an integrated technical framework of"geometric correction-image enhancement-intelligent recognition",systematically addressing key technical challenges in transmission line image recognition.In the geometric correction stage,a polynomial geometric correction model based on the least squares method was employed.By establishing an accurate coordinate mapping,this model effectively eliminated geometric distortions caused by factors such as shooting angles and lens distortion.In the image enhancement stage,a new image processing algorithm,combining bilateral filtering and the maximum between-class variance method,was proposed.This algorithm effectively removed image noise while retaining the edge features of transmission lines,providing high-quality data for subsequent recognition.In the intelligent recognition stage,a dual-optimized convolutional neural network(CNN)model was designed.The feature extraction process was optimized by dynamically adjusting the convolution kernel weights,and sparse constraints were introduced to enhance feature discriminability.Finally,precise recognition was achieved by integrating the support vector machine classifier.This method overcame the limitations of traditional technologies,such as insufficient geometric distortion correction and feature extraction,offering a comprehensive solution for intelligent identification of hidden dangers of transmission lines.[Results]Tested on real datasets containing multiple types of damage,this method demonstrates significantly higher recognition accuracy compared to mainstream algorithms such as YOLOv4 and Mask R-CNN.It shows greater robustness,especially in complex backgrounds.The method achieves an average positional offset of only 0.013 meters,fully meeting engineering application requirements.The floating point operations for processing 1 000 images reduce to 3.24 × 109,significantly enhancing the real-time processing capability.[Conclusions]The proposed intelligent recognition technology for external damage risks in transmission lines has made significant improvements in recognition accuracy,positioning precision,and computational efficiency through innovative technical approaches and systematic optimizations.The theoretical contributions of this research include establishing a complete image processing system for transmission lines,providing a new approach for related studies;introducing a dual optimization mechanism that offers a viable solution for feature extraction in complex environments;adopting the lightweight network design,which serves as an important reference for applying deep learning models in engineering.
Keywords：transmission lineconvolutional neural networkexternal damagetrigger source image recognitionimage enhancementbilateral filteringmaximum between-class variancehidden danger feature
Publication Date：2025-11-25
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：9( 808-816 )