Deep Learning Method to Detect and Locate Signages from 2D Drawings for Semantic Enrichment of BIM
Publication: Computing in Civil Engineering 2023
ABSTRACT
A reasonable signage layout can reduce evacuees’ operational delays during emergencies. Building information modeling (BIM) with detailed signage information is essential for automatic checking and improving the signage layout. However, in current practice, exit signs of buildings are presented mostly in 2D drawing formats rather than in BIM. Manually adding these signages into BIM is time-consuming and labor-intensive, and reconstructing them with on-site data requires considerable device costs and workload. Hence, there is a need for a convenient, low-cost, and widely applicable approach to extract signage information from 2D drawings and add them into BIM. Related works on 2D drawings mainly focused on architectural and structural components, but those methods cannot directly apply to exit signs. This study proposes a deep learning method combined with pre-set rules to detect and locate exit signs from 2D drawings. The extracted information is expected to enrich existing building models.
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Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 535 - 543
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Architectural engineering
- Architecture
- Artificial intelligence and machine learning
- Benefit cost ratios
- Building information modeling
- Building management
- Buildings
- Business management
- Computer programming
- Computing in civil engineering
- Detection methods
- Disaster preparedness
- Disaster risk management
- Emergency management
- Employment
- Engineering fundamentals
- Financial management
- Labor
- Methodology (by type)
- Neural networks
- Personnel management
- Practice and Profession
- Structural engineering
- Structures (by type)
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