基于动态Token化关系Transformer的工程图端到端超关系信息抽取
End-to-End Hyper-Relational Information Extraction for Engineering Diagrams via Dynamically Tokenized Relation Transformer
DTRT 用动态 token 缩减与关系 Transformer 端到端地从高分辨率工程图中抽取符号、连线与文本之间的超关系，可直接迁移到需要同时定位元件并建模拓扑连接的场景（如电路图、流程图解析），其动态 token 剪枝策略对高分辨率图像的高效 Transformer 推理也有参考价值。
原文依据 dynamically reduce received image tokens, filter redundant information, and efficiently extract structural knowledge to construct hyper-relational knowledge graphs
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论工程图是工业场景中技术信息的核心载体，工业界对其数字化的迫切需求推动了相关研究领域的巨大进展。然而，现有研究仍存在三方面局限。首先，符号、线条和文本的检测通常涉及多个独立模型，导致工作流程繁琐。此外，高分辨率图纸往往给现有模型带来过高的计算成本。再者，仅基于目标检测的解析框架只能定位组件位置，却无法捕捉组件间的拓扑连接语义与结构化知识，因而对工业应用的便利性有限。为解决这些问题，我们提出了一种基于动态Token化关系Transformer（DTRT）的端到端信息抽取框架，该框架能够动态缩减接收到的图像Token、过滤冗余信息，并高效抽取结构化知识以构建超关系知识图谱。我们在管道及仪表图（P&ID）和电气图（ED）上实践了该模型：前者广泛应用于化工企业，后者用于描述电路系统。DTRT在P&ID上达到94.84%的R@1000准确率，在ED上达到92.52%的R@200准确率，同时计算成本显著降低。我们的代码已公开于https://github.com/Tianyou-Bai/DTRT-Diagram-Parsing-in-Scene-Graph。
英文原文摘要 1567 字符
Engineering diagrams are the core carriers of technical information in industrial contexts, where the pressing demand for their digitization from industrial sectors has driven great advancements in related research domains. However, existing research still suffers from three limitations. Firstly, the detection of symbols, lines, and texts typically involves multiple independent models, resulting in cumbersome workflows. In addition, high-resolution diagrams often impose an excessive computational cost on existing models. Moreover, parsing frameworks solely based on object detection can merely localize component positions, yet fail to capture the topological connection semantics and structured knowledge among components, thus offering limited convenience for industrial applications. To address these issues, we propose an end-to-end information extraction framework based on the Dynamically Tokenized Relation Transformer (DTRT), which can dynamically reduce received image tokens, filter redundant information, and efficiently extract structural knowledge to construct hyper-relational knowledge graphs. We practiced our model on piping and instrumentation diagrams (P&IDs) and electrical diagrams (EDs): the former are widely used in chemical engineering enterprises, while the latter are employed to describe circuit systems. DTRT achieves an R@1000 accuracy of 94.84% on PIDs and R@200 accuracy of 92.52% on EDs with a significantly reduced computational cost. Our code is available at https://github.com/Tianyou-Bai/DTRT-Diagram-Parsing-in-Scene-Graph.