SkeySpot: Automating Service Key Detection for Digital Electrical Layout Plans in the Construction Industry
Dhruv Dosi, Rohit Meena, Param Rajpura, Yogesh Kumar Meena

TL;DR
SkeySpot is an open-source tool that automates the detection and classification of electrical symbols in scanned floor plans, enhancing digitization and interoperability in construction workflows.
Contribution
This work introduces a new annotated dataset, benchmarks object detection models, and develops a lightweight toolkit for electrical symbol recognition in scanned plans.
Findings
YOLOv8 achieves 82.5% mAP on the DELP dataset.
SkeySpot enables real-time detection and classification of electrical symbols.
The approach reduces manual effort and enhances standardization in electrical layout digitization.
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…
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