用真实拓扑生成合成图纸，让模型在无真实数据下也能精准识别工艺图管线。
SynthPID: P&ID digitization from Topology-Preserving Synthetic Data
- 基于真实图纸拓扑生成合成工艺图，保持真实布局结构。
- 仅用合成数据训练，边缘检测准确率达63.8%，接近真实数据模型。
- 揭示合成数据质量比数量更重要，400张后性能趋于饱和。
自动化将管道与仪表图（P&IDs）转化为结构化流程图可显著提升工厂运营价值，但受限于数据稀缺：工程图纸属专有信息，公开基准仅含12张标注图像。以往的合成数据增强方法因模板生成符号随机分布，导致图结构与真实工厂差异大，仅达约33%的边检测准确率。本文提出SynthPID，构建包含665张合成P&ID的语料库，其管道拓扑直接源自真实图纸。结合专为高分辨率图设计的patch-based Relationformer，仅用合成数据训练的模型在PID2Graph OPEN100上达到63.8±3.1%的边mAP，距离真实数据基线仅差8个百分点。控制实验表明，性能提升源于生成质量而非模型选择。规模研究显示，超过约400张合成图后收益趋于平缓，提示种子多样性是主要瓶颈。
原文摘要 · Abstract (English)
Automating the digitization of Piping and Instrumentation Diagrams (P&IDs) into structured process graphs would unlock significant value in plant operations, yet progress is bottlenecked by a fundamental data problem: engineering drawings are proprietary, and the entire community shares a single public benchmark of just 12 annotated images. Prior attempts at synthetic augmentation have fallen short because template-based generators scatter symbols at random, producing graphs that bear little resemblance to real process plants and, accordingly, yield only approximately 33% edge detection accuracy under synth-only training. We argue the failure is structural rather than visual and address it by introducing SynthPID, a corpus of 665 synthetic P&IDs whose pipe topology is seeded directly from real drawings. Paired with a patch-based Relationformer adapted for high-resolution diagrams, a model trained on SynthPID alone achieves 63.8 +/- 3.1% edge mAP on PID2Graph OPEN100 without seeing a single real P&ID during training, closing within 8 pp of the real-data oracle. These gains hold up under a controlled comparison against the template-based regime, confirming that generation quality drives performance rather than model choice. A scaling study reveals that gains flatten beyond roughly 400 synthetic images, pointing to seed diversity as the binding constraint.
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