Recognition of House Structures from Complicated Electrical Plan Images

Abstract

Advances in image analysis and deep learning technologies have expanded the use of floor plans, traditionally used for sales and rentals, to include 3D reconstruction and automated design. However, a typical floor plan does not provide detailed information, such as the type and number of outlets and locations affecting the placement of furniture and appliances. Electrical plans, providing details on electrical installations, are intricate due to overlapping symbols and lines and remain unutilized as house manufacturers independently manage them. This paper proposes an analysis method that extracts the house structure, room semantics, connectivities, and specifics of wall and ceiling sockets from electrical plans, achieving robustness to noise and overlaps by leveraging the unique features of symbols and lines. The experiments using 544 electrical plans show that our method achieved better accuracy (+3.6 pt) for recognizing room structures than the state-of-the-art method, 87.2% in identifying room semantics and 97.7% in detecting sockets.