Electrical Equipment Segmentation in Complex Substation Scenarios Based on Improved Transformer

Abstract

Purposes Owing to the varietry of electrical equipment and the complex connection 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 practical applications, and inaccurate electrical equipment image segmentation brought by the traditional 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 (Substation 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 experimented on the dataset of Liangjiazhuang Transformer Station in Shanxi province, and its longitudinal 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.

Keywords

• transformer
• cnn
• image segmentation
• electrical equipment
• substation