微型电脑应用2025，Vol.41Issue(2)：55-60,6.
基于数据增强和注意力机制的输电线异物检测算法的研究
Research on Obstructive Object Detection Algorithm of Transmission Line Based on Data Enhancement and Attention Mechanism
摘要
Abstract
There are many kinds but few data in the training data set of the transmission line obstructive object model,so that existing methods have the situation that the detailed features are lost in the process of deep network feature learning.In this pa-per,a single shot multibox detector(SSD)framework with data enhancement and attention enhancement mechanism is pro-posed to detect foreign bodies in power lines.The collected images of foreign bodies in power lines are preprocessed,which mainly includes Gaussian denoising of particle noise and then histogram equalization.The Mosaic method is used to expand the foreign body model training dataset of transmission lines to improve the robustness and generalization ability of the foreign body detection model.The attention mechanism squeeze-and-excitation(SE)network module is introduced into the SSD detection framework,which can efficiently learns the features between different channels and perform feature fusion,so as to extract the key feature information accurately and quickly.The testing results show that the proposed foreign body detection algorithm based on data enhancement and attention mechanism can detect foreign bodies in power lines more accurately.Compared with Faster RCNN,SSD and YOLOv3 detection algorithms,the detection speed of the model is improved by 5 percentage points,3 percentage points and 6 percentage points,and the detection speed of model is reduced by 0.021 s,0.007 s and 0.003 s,re-spectively.关键词
输电线异物检测/Mosaic数据增强/SSD目标检测/注意力机制/SE网络Key words
obstructive object detection of transmission line/Mosaic data enhancement/SSD target detection/attention mecha-nism/SE network分类
信息技术与安全科学引用本文复制引用
齐国营,高彦飞,李剑武,吴永华..基于数据增强和注意力机制的输电线异物检测算法的研究[J].微型电脑应用,2025,41(2):55-60,6.基金项目
国家自然科学基金项目(52061042,U1134106) （52061042,U1134106）
中国华能集团有限公司课题项目(HNKJ21-HF252) （HNKJ21-HF252）
