用Transformer统一提取管道图符号与连接关系，准确率提升超25%
From Engineering Diagrams to Graphs: Digitizing P&IDs with Transformers
- 基于Relationformer的端到端方法，联合识别符号与连接
- 在真实图纸上边检测准确率比传统分步法提升25%以上
- 提供首个带图级标注的公开数据集，适合工业视觉研究
将工程图纸如管道仪表图（P&ID）数字化对流程与液压系统的维护性和运行效率至关重要。以往方法通常分步进行符号检测和连线检测，难以捕捉图中结构信息。本文提出一种基于Transformer的Relationformer方法，能联合从P&ID中提取符号及其相互连接。为评估该方法并对比模块化方案，我们构建了首个公开可用的P&ID数字化基准数据集，包含图级真值标注。在真实图纸上的实验表明，该方法显著优于模块化基线，边检测准确率提升超过25%。本研究提供了可复现的评估框架，验证了Transformer模型在复杂工程图结构理解中的有效性。数据集已发布于https://zenodo.org/records/14803338。
原文摘要 · Abstract (English)
Digitizing engineering diagrams like Piping and Instrumentation Diagrams (P&IDs) plays a vital role in maintainability and operational efficiency of process and hydraulic systems. Previous methods typically decompose the task into separate steps such as symbol detection and line detection, which can limit their ability to capture the structure in these diagrams. In this work, a transformer-based approach leveraging the Relationformer that addresses this limitation by jointly extracting symbols and their interconnections from P&IDs is introduced. To evaluate our approach and compare it to a modular digitization approach, we present the first publicly accessible benchmark dataset for P&ID digitization, annotated with graph-level ground truth. Experimental results on real-world diagrams show that our method significantly outperforms the modular baseline, achieving over 25% improvement in edge detection accuracy. This research contributes a reproducible evaluation framework and demonstrates the effectiveness of transformer models for structural understanding of complex engineering diagrams. The dataset is available under https://zenodo.org/records/14803338.
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