构建首个大规模印制电路图数据集，实现自动从图中提取电路连接信息。
PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images
- 结合视觉识别与领域知识，自动解析电路图并生成SPICE网表。
- 组件检测准确率94.54%，文字识别达98.57%，连接关系正确率84.47%。
- 适合电子设计自动化、AI辅助电路设计研究者使用。
印刷电路板（PCB）是现代电子系统的基础，但人工智能驱动的PCB设计自动化仍受限于缺乏大规模成对的原理图-网表数据集。原理图因器件类型多样、布线拓扑复杂及文本标注噪声大而难以处理。为此，我们提出PCBnet，一个包含300多个真实设计的大规模原理图数据集，涵盖超过50,000个器件实例、150,000条连线、100,000个文本区域和400,000个字符，并配有标注引脚和对应的SPICE网表。我们进一步开发了一套自动化原理图到网表转换流程，融合视觉识别、拓扑构建与基于领域知识的多智能体校正。所提方法在组件检测上达到94.54% mAP，文字识别准确率为98.57%，端到端连接准确率达84.47%。PCBnet为未来AI驱动的PCB设计自动化提供了基准与数据基础。
原文摘要 · Abstract (English)
Printed circuit boards (PCBs) are fundamental to modern electronic systems, yet AI-driven PCB design automation remains constrained by the lack of large-scale paired schematic-netlist datasets. PCB schematics are particularly challenging due to diverse component types, complex wiring topologies, and noisy textual annotations. To address this gap, we present PCBnet, a large-scale PCB schematic dataset comprising over 300 real-world designs with annotated pins and paired SPICE netlists. It contains more than 50,000 component instances, 150,000 wires, 100,000 text regions, and 400,000 characters. We further develop an automated schematic-to-netlist pipeline that combines visual recognition, topology construction, and domain-knowledge-guided multi-agent correction. The proposed method achieves 94.54% component detection mAP, 98.57% text recognition accuracy, and 84.47% end-to-end connectivity accuracy. PCBnet provides a benchmark and data foundation for future AI-driven PCB design automation.
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