 A U-net convolutional neural network deep learning model application for identification of energy loss in infrared thermographic imagesDavid Gertsvolf, Miljana Horvat, Danesh Aslam, April Khademi and Umberto Berardi Applied Energy, 2024, vol. 360, issue C, No S0306261924000795 Abstract: The possibility of obtaining large data set of infrared images during building and urban envelope surveys require the development of fast and effective ways to process their content. This study presents a novel U-NET convolution neural network (CNN) deep learning (DL) model for the identification of envelope deficiencies on a data set of infrared (IR) thermographic images of building envelopes. A data set of images acquired with an unmanned aerial vehicle (UAV) were used with supplementary segmentation masks created for appropriate U-NET modelling application. This data preparation process is presented followed by an in-depth review of the CNN architecture used for the segmentation process. The Python3 code developed for this study is simplified for easier application by non-data-science researchers. The results of this research show high accuracy. However, large data set are needed to better train the CNN-DL model. Keywords: Infrared thermography; Building envelope assessment; Deep learning; Convolution neural network; U-NET (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:360:y:2024:i:c:s0306261924000795 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2024.122696 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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