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<p>Substation engineering drawing parsing is essential for the automation, intelligence, and digital transformation of power systems. However, existing methods face significant challenges due to the complexity of these drawings and the limited availability of bitmap datasets. The drawings contain dense lines, specialized symbols, and intricate layouts, making it difficult for traditional object detection models to accurately identify components and text, often resulting in high rates of false positives and false negatives. Additionally, the lack of unified data standards leads to overfitting during model training, limiting generalization across diverse scenarios. To address these issues, we propose an integrated framework combining object detection and OCR for intelligent substation drawing analysis. Our method employs a dense random data augmentation matching strategy and an improved semantic alignment strategy to enhance feature robustness and model adaptability while maintaining computational efficiency. We also introduce a new dataset of 600 annotated substation engineering drawing images, covering various layout types and textual elements. Our experimental results show that our proposed method significantly outperforms existing techniques, achieving AP50, AP75, and mAP scores of 89.40%, 89.30%, and 63.00%, respectively. This demonstrates the effectiveness of our approach in accurately parsing complex substation drawings and contributes to the advancement of power systems’ automation and digital transformation.</p>

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Substation drawing intelligent parsing framework with dense augmentation and semantic alignment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Tong Yan,
  • Chaoyi Zhu,
  • Xinhui Zhang,
  • Yiheng Zeng

摘要

Substation engineering drawing parsing is essential for the automation, intelligence, and digital transformation of power systems. However, existing methods face significant challenges due to the complexity of these drawings and the limited availability of bitmap datasets. The drawings contain dense lines, specialized symbols, and intricate layouts, making it difficult for traditional object detection models to accurately identify components and text, often resulting in high rates of false positives and false negatives. Additionally, the lack of unified data standards leads to overfitting during model training, limiting generalization across diverse scenarios. To address these issues, we propose an integrated framework combining object detection and OCR for intelligent substation drawing analysis. Our method employs a dense random data augmentation matching strategy and an improved semantic alignment strategy to enhance feature robustness and model adaptability while maintaining computational efficiency. We also introduce a new dataset of 600 annotated substation engineering drawing images, covering various layout types and textual elements. Our experimental results show that our proposed method significantly outperforms existing techniques, achieving AP50, AP75, and mAP scores of 89.40%, 89.30%, and 63.00%, respectively. This demonstrates the effectiveness of our approach in accurately parsing complex substation drawings and contributes to the advancement of power systems’ automation and digital transformation.