用边缘图增强视觉语言模型，让工业流程图转代码更准
EdgeFlow: Edge-Map Augmented VLM-Based Flowchart Processing for Industrial Requirements Engineering
- 输入流程图时叠加提取的边缘图作为结构先验
- 节点、边、路径的准确率分别提升17.39、16.94、11.06个百分点
- 无需训练数据，适合工业需求工程场景快速部署
流程图在工业需求工程中广泛应用，但通常以静态图像形式存在。视觉语言模型（VLMs）有望将其转换为可机器读取的模型，但直接应用时常因忽略拓扑关键视觉细节而失败。为此，我们提出EdgeFlow，通过确定性提取的Canny边缘图作为结构先验，增强VLM输入，从而提升流程图到Mermaid的转换精度，且无需标注数据或领域微调。我们在真实工业需求数据集IndusReqFlow上评估，相比现成VLMs，EdgeFlow在节点级F1提升17.39个百分点，边级提升16.94个百分点，路径级提升11.06个百分点，显著支持基于模型的测试。跨数据集在公开合成基准上未见显著提升，凸显未来评估需引入更多工业数据的多样化基准。
原文摘要 · Abstract (English)
Flowcharts are widely used in industrial requirements, but usually remain embedded as static images. Vision Language Models (VLMs) show promise in the conversion of these flowcharts into machine-readable models for RE activities, yet, when directly applied to flowchart conversion, they often fail on topology-critical visual details. To address this, we propose EdgeFlow that augments a VLM's original input with a deterministically extracted Canny edge map-acting as a structural prior-to improve flowchart-to-Mermaid conversion, without requiring annotated training data or domain-specific model fine-tuning. We evaluate EdgeFlow on IndusReqFlow, a dataset sourced from real-world requirements. Compared with off-the-shelf VLMs, EdgeFlow improves node-level F1 by 17.39 percentage points and edge-level F1 by 16.94 percentage points. At the path level, EdgeFlow improves path F1 by 11.06 percentage points, enabling better support for model-based testing. These results demonstrate that EdgeFlow provides a practical, training-free means to improve topology-preserving flowchart-to-Mermaid conversion for industrial RE. Cross-dataset evaluation results on a public synthetic benchmark show no significant improvement; this highlights the need for diverse benchmarks incorporating industrial data for the comprehensive evaluation of future VLM-based RE tools.
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