Domain-Adaptive Object Detection of Electrical Facilities for Enhanced Semantic Indoor Models

Abstract

Detecting visible electrical utilities is a prerequisite for developing advanced reasoning strategies to reconstruct hidden in-wall networks. This paper investigates the detection of visible power-related utilities using a domain-adaptive deep learning-based vision pipeline based on the YOLOv11-L, object detection model. Four publicly available datasets containing power sockets, power strips, and light switches were curated, relabeled, and merged into a unified training dataset of 3,459 images. The resulting model achieved a mean average precision (mAP) of 0.74 for power sockets and strips and 0.98 for light switches, demonstrating strong detection performance. Real-time evaluation on a low-cost smartphone via the Ultralytics HUB App indicates reliable detection in small-scale real-world environments and detected utilities could be integrated automatically into semantic indoor models using a marker-less referencing approach.The work further highlights broader applications, including Augmented Reality-based visualization to reduce cognitive load for project managers and inspectors or construction workers and electricians, and its potential use as input for existing and future reasoning methods for hidden-utility reconstruction. The prepared dataset, trained model and source code is available at: https://github.com/hcu-cml/indoor-electrical-facility-detection.