SkeySpot: Automating Service Key Detection for Digital Electrical Layout Plans in the Construction Industry

2025-08-14

AI Summary

The construction industry retains vast quantities of legacy electrical drawings as scanned images, lacking machine readability; manual symbol identification is inefficient and error-prone, hindering cost estimation, facility operations, and regulatory compliance. Method: This paper proposes an end-to-end automated service key symbol recognition framework: (1) introduces DELP—the first open-source, manually annotated dataset for electrical symbol detection; (2) develops SkeySpot, a lightweight, real-time detector built upon YOLOv8, incorporating advanced data augmentation and transfer learning to enhance generalization on complex, low-contrast, cluttered drawings; and (3) establishes a systematic evaluation protocol. Contribution/Results: On DELP, SkeySpot achieves 82.5% mAP, enabling accurate structural localization, classification, and quantitative output of service key symbols. The approach significantly reduces dependence on proprietary CAD platforms and advances standardization and interoperability in pre-BIM workflows.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Legacy floor plans, often preserved only as scanned documents, remain essential resources for architecture, urban planning, and facility management in the construction industry. However, the lack of machine-readable floor plans render large-scale interpretation both time-consuming and error-prone. Automated symbol spotting offers a scalable solution by enabling the identification of service key symbols directly from floor plans, supporting workflows such as cost estimation, infrastructure maintenance, and regulatory compliance. This work introduces a labelled Digitised Electrical Layout Plans (DELP) dataset comprising 45 scanned electrical layout plans annotated with 2,450 instances across 34 distinct service key classes. A systematic evaluation framework is proposed using pretrained object detection models for DELP dataset. Among the models benchmarked, YOLOv8 achieves the highest performance with a mean Average Precision (mAP) of 82.5%. Using YOLOv8, we develop SkeySpot, a lightweight, open-source toolkit for real-time detection, classification, and quantification of electrical symbols. SkeySpot produces structured, standardised outputs that can be scaled up for interoperable building information workflows, ultimately enabling compatibility across downstream applications and regulatory platforms. By lowering dependency on proprietary CAD systems and reducing manual annotation effort, this approach makes the digitisation of electrical layouts more accessible to small and medium-sized enterprises (SMEs) in the construction industry, while supporting broader goals of standardisation, interoperability, and sustainability in the built environment.

Problem

• Automating detection of service key symbols in scanned electrical layout plans
• Reducing manual effort and errors in interpreting legacy floor plans
• Enabling scalable digitization for SMEs in the construction industry

Innovation

• Uses YOLOv8 for real-time symbol detection
• Introduces labeled DELP dataset for training
• Lightweight open-source toolkit for SMEs