Projects per year
Abstract
Scan-to-BIM systems convert image and point cloud data into accurate 3D models of buildings. Research on Scan-to-BIM has largely focused on the automated identification of structural components. However, design and maintenance projects require information on a range of other assets including mechanical, electrical, and plumbing (MEP) components. This paper presents a deep learning solution that locates and labels MEP components in 360deg images and phone images, specifically sockets, switches and radiators. The classification and location data generated by this solution could add useful context to BIM models. The system developed for this project uses transfer learning to retrain a Faster Region-based Convolutional Neural Network (Faster R-CNN) for the MEP use case. The performance of the neural network across image formats is investigated. A dataset of 249 360deg images and 326 phone images was built to train the deep learning model. The Faster R-CNN achieved high precision and comparatively low recall across all image formats.
Original language | English |
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Title of host publication | Pattern Recognition |
Subtitle of host publication | ICPR International Workshops and Challenges |
Publisher | Springer |
Pages | 373-388 |
Volume | 12667 |
ISBN (Electronic) | 978-3-030-68787-8 |
ISBN (Print) | 978-3-030-68786-1 |
DOIs | |
Publication status | Published - 21 Feb 2021 |
Event | Pattern Recognition and Automation in Construction & the Built Environment - Milan, Italy Duration: 10 Jan 2021 → … https://praconbe2020.iti.gr/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 12667 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Workshop
Workshop | Pattern Recognition and Automation in Construction & the Built Environment |
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Abbreviated title | PRAConBE |
Country/Territory | Italy |
City | Milan |
Period | 10/01/21 → … |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Scan-to-BIM
- MEP
- Radiators
- Sockets
- Switches
- Convolutional Neural Network
- Deep learning
Fingerprint
Dive into the research topics of 'Automatic MEP Component Detection with Deep Learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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BIMERR: BIM-based holistic tools for Energy-driven Renovation of existing Residences
Bosche, F. & Valero Rodriguez, E.
1/04/19 → 30/09/22
Project: Research