Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams

The digitization of electrical diagrams plays a crucial role in modern construction industries, enabling efficient reuse, seamless distribution, and accurate archiving. Despite technological advances, many of these diagrams remain in undigitized formats, leading to labor-intensive manual analysis for tasks such as cost estimation and wiring design. These challenges are aggravated by the diversity of symbols, high inter-class similarities, and the inherent complexities of wiring layouts, which require advanced recognition and efficient wiring design. This paper presents a deep learning framework that integrates an attention mechanism for symbol recognition, followed by a graph-based algorithm for fully automated wiring design. Through comparative evaluation, Efficient Channel Attention emerged as the most effective attention module, improving the mean average precision by 3.2%. The wiring algorithm leverages an improved pathfinding approach that reduces bends and total wiring length by 43% while adhering to boundary constraints and electrical rules. Extensive experiments on proprietary and public datasets demonstrate that the proposed framework significantly improves the recognition of complex electrical symbols, outperforming the baseline model. This research sets a new benchmark for automating electrical diagram analysis, offering substantial cost savings while reducing the manual effort associated with large-scale construction projects.