Practical single-line diagram recognition based on digital image processing and deep vision models

Abstract

The Single-Line Diagram (SLD) is a standardized graphical representation of power systems. Power engineering software typically requires users to model power systems by drawing SLDs. Although existing computer vision-based SLD recognition approaches facilitate system modeling to some extent, their performance is usually limited by the low resolution of images, the imbalance in size and number of electrical symbols, and the interference between adjacent graphical elements on practical SLDs. This paper proposes a novel SLD recognition framework to address these challenges. First, a text and symbol separation module based on digital image processing is developed to enable independent analysis of text labels and electrical symbols to avoid mutual interference. To accurately recognize electrical symbols of different sizes, the proposed framework integrates the Swin Transformer into the Faster Region-based Convolutional Neural Network (Faster R-CNN), which ultimately achieves a minimum precision of 98.9% and a minimum recall of 98.12%. In addition, the framework modifies the Text Super-Resolution Network (TSRN) to improve text recognition performance on low-resolution images. Combined with the Attentional Scene Text Recognizer with Flexible Rectification (ASTER) model, the proposed text recognition model results in an average accuracy increase of 1% on the TextZoom dataset and 97.1% accuracy in recognizing practical SLD text labels. A synthetic SLD generation method is also proposed to augment the actual SLD datasets to overcome the class imbalance of electrical symbols. The proposed framework significantly improves the performance of existing engineering drawing recognition frameworks. Relevant source code is available online at https://github.com/Lattle-y/work.git.