Electrical wiring diagram recognition via topology‑Aware graph learning based on CAD data parsing

Abstract

Intelligent recognition of substation building electrical wiring diagrams is a key step to ensure the correctness and safety of substation design and to improve the efficiency of engineering operations. However, existing methods struggle with severe text-graphic interference, extreme scale variations of symbols, and their inability to explicitly model complex, imbalanced topological connections. To solve these challenges, this paper proposes a novel framework for electrical wiring drawing recognition, formulated as a topology-aware graph learning problem. First, we propose a CAD-image fusion strategy that leverages deterministic vector topology to guide visual parsing, effectively suppressing text-graphic interference to extract high-fidelity physical wiring structures. Second, by formalizing heterogeneous devices and their physical connections into a formal graph data structure, we develop a GraphSAGE-based classifier capable of decoding the complex, multi-hop configurations of complete electrical assemblies. Last, to handle unique geometric variations and structural sparsity, domain-tailored optimizations are introduced, including Dynamic Snake Convolution (DSConv) for capturing elongated busbars and a Gated-Attention mechanism to regulate extreme topological imbalance during graph aggregation. Experimental results on real-world 110 kV substation diagrams demonstrate the effectiveness of our framework, achieving 96.4% mAP for element detection, 98.95% edge precision for topology extraction, and 95.47% accuracy for assembly classification.