Data supporting figures and tables in Reimagining Electrical Diagrams in Construction: Automated Symbol Detection and Wiring Design and Generation with Deep Learning

OverviewThis dataset supports the findings of the research titled “Reimagining Electrical Diagrams in Construction: Automated Symbol Detection and Wiring Design and Generation with Deep Learning.”
The study presents a fully automated framework that digitises complex electrical diagrams through two main components:
Symbol Recognition: A YOLOv8-based deep learning model trained to recognise 30 electrical symbol classes in industrial diagrams.Automated Wiring Design: A modified A* pathfinding algorithm that generates orthogonal wiring between recognised symbols, reducing total wire length by 44% compared to traditional methods.The dataset includes preprocessing experiments such as data augmentation (AUG) and low-intensity sampling (LINS) to improve detection performance and mitigate class imbalance.
Datasets IncludedThis data bundle contains the primary files used to produce figures and tables within the manuscript, including:
Symbol distribution and augmentation statistics (Table 1).Model performance metrics across preprocessing experiments (YOLOv7 and YOLOv8) (Tables 3–5).Wiring algorithm evaluation results comparing the multiline plotter and modified A* methods (Table 6).Hardware and software configuration details (Table 2).Examples of detected symbols, wiring paths, and recognition visualisations (Figures 1–7).Each table is provided in CSV format, mapping data files directly to their corresponding figure or table in the paper.
Use CasesThis dataset can be used for:
Benchmarking YOLO-based models for electrical symbol recognition in high-resolution engineering diagrams.Studying the impact of class imbalance, augmentation, and preprocessing techniques on detection accuracy.Evaluating automated wiring algorithms using modified A* search for layout optimisation.Reproducing experimental setups for symbol recognition and routing in industrial diagram analysis.