太原理工大学学报2024，Vol.55Issue(1)：57-65,9.DOI:10.16355/j.tyut.1007-9432.20230218
基于改进Transformer的变电站复杂场景下电力设备分割
Electrical Equipment Segmentation in Complex Substation Scenarios Based on Improved Transformer
摘要
Abstract
[Purposes]Owing to the varietry of electrical equipment and the complex connec-tion between them in transformer station,there are many common problems includeng relatively limited location and picture contrast of equipment,insufficient target images and markers in prac-tical applications,and inaccurate electrical equipment image segmentation brought by the tradi-tional way.In this paper,CNN(Convolutional Neural Network)is combined with Transformer to form a new model for segmentation of electrical equipment,and a new SE-Transfomer(Sub-station Equipment Transformer)network based on codec structure is proposed.[Methods]To obtain the local context information,the coder extracts the spatial feature map by using CNN at first.Meanwhile,the feature map is carefully modified with multi-scale feature inputs for global feature modeling.The decoder extracts global deep features using Transformer and performs stepwise up-sampling to predict the detailed segmentation map.SE-Transfomer is extensively ex-perimented on the dataset of Liangjiazhuang Transformer Station in Shanxi province,and its lon-gitudinal results of Dice,Recall,Specificity,and RMSE(Root Mean Square Error)are 89.31％,90.52％,89.62％,and 11.32,respectively.[Findings]The results indicate that SE-Transfomer obtains comparable or higher results than previous state-of-the-art segmentation methods on the scanning of electrical equipment in the transformer station.关键词
Transformer/CNN/图像分割/电力设备/变电站Key words
transformer/CNN/image segmentation/electrical equipment/substation分类
计算机与自动化引用本文复制引用
李洋,朱春山,张建亮,高伟,薛泓林,马军伟,温志芳..基于改进Transformer的变电站复杂场景下电力设备分割[J].太原理工大学学报,2024,55(1):57-65,9.基金项目
国网山西省电力公司科技项目资助(52051C220003) （52051C220003）
