Intelligent Detection of Mechanical, Electrical, and Plumbing (MEP) Metrics Based on 2D Floor Plans
This research developed a neural network-based model to extract various information from 2D floor plans. We detect lighting symbols, identify the appropriate type of light, and extract the associated texts with lights. The study aims to enable efficient floor designing and determining the number and type of lights needed per floor, i.e., allow efficient design and estimate the power requirement of the floor plan. The model was developed using Mask RCNN as the base. The images were annotated and converted into a Coco data format for training the model. The model achieved bbox\_mAP and segm\_mAP
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- PossiblePossibly related (embedding) · 48%Poly R-CNN: Efficient large-scale boundary-regularized building footprint extraction from remote sensing images - Springer Nature Link →
- LinkedLinked via arxiv author · 85%Tarandeep Singh Mandhiratta →
“Intelligent Detection of Mechanical, Electrical, and Plumbing (MEP) Metrics Based on 2D Floor Plans”
- LinkedLinked via arxiv author · 85%ANK Zaman →
“Intelligent Detection of Mechanical, Electrical, and Plumbing (MEP) Metrics Based on 2D Floor Plans”
- LinkedLinked via arxiv author · 85%Abdul-Rahman Mawlood-Yunis →
“Intelligent Detection of Mechanical, Electrical, and Plumbing (MEP) Metrics Based on 2D Floor Plans”