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Building facade evaluation using instance segmentation on thermal images
Citation Link: https://doi.org/10.15480/882.13553
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
TORE-DOI
Start Page
74
End Page
81
Citation
35. Forum Bauinformatik, fbi 2024: 74-81
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
To advance building energy efficiency assessments, Deep Learning (DL) technologies such as image segmentation can be leveraged to expedite evaluations. Previous research has applied instance segmentation to compute U-values of doors, walls, windows and facades, or has applied semantic segmentation to identify thermal anomalies. This work focused on training a DL instance segmentation model on a rudimentary dataset to discern and categorize different facade elements, including windows, doors, walls, floors, and thermal anomalies. The fine-tuned Mask RCNN ResNet-50+FPN model yielded the following segmentation Average Precision (AP) values: 45.6 (mean AP), 65.2 (window glazing), 53.5 (window), 34.9 (wall) and 17.2 (thermal bridge). The bounding box AP for thermal bridges, 28.6, highlights the model’s ability to detect thermal bridges. The results presented in this paper indicate the potential for applying DL instance segmentation to thermal images to identify thermal bridges and estimate U-values using the Infrared Thermovision Technique (ITT). The dataset can be found in https://github.com/gerdac/thermal-images-instance.
Subjects
dataset
deep learning
instance segmentation
Thermal performance assessment
DDC Class
006: Special computer methods
621.3: Electrical Engineering, Electronic Engineering
624: Civil Engineering, Environmental Engineering
Publication version
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Building facade evaluation using instance segmentation on thermal images.pdf
Type
Main Article
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Format
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