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Enhancing Building Facade Image Segmentation via Object-Wise Processing and Cascade U-Net

Haemin Jung1, Heesung Park2, Hae Sun Jung3, Kwangyon Lee4,*

1 Department of Industrial & Management Engineering, Korea National University of Transportation, Chungju, 27469, Republic of Korea
2 Department of Industrial Engineering, Yonsei University, Seoul, 03722, Republic of Korea
3 Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, 03063, Republic of Korea
4 School of Electronic Engineering, Soongsil University, Seoul, 06978, Republic of Korea

* Corresponding Author: Kwangyon Lee. Email: email

Computers, Materials & Continua 2024, 81(2), 2261-2279. https://doi.org/10.32604/cmc.2024.057118

Abstract

The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades, as buildings contribute significantly to energy consumption in urban environments. However, conventional image segmentation methods often struggle to capture fine details such as edges and contours, limiting their effectiveness in identifying areas prone to energy loss. To address this challenge, we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model, Cascade U-Net. Object-wise processing isolates components of the facade, such as walls and windows, for independent analysis, while Cascade U-Net incorporates contour information to enhance segmentation accuracy. The methodology involves four steps: object isolation, which crops and adjusts the image based on bounding boxes; contour extraction, which derives contours; image segmentation, which modifies and reuses contours as guide data in Cascade U-Net to segment areas; and segmentation synthesis, which integrates the results obtained for each object to produce the final segmentation map. Applied to a dataset of Korean building images, the proposed method significantly outperformed traditional models, demonstrating improved accuracy and the ability to preserve critical structural details. Furthermore, we applied this approach to classify window thermal loss in real-world scenarios using infrared images, showing its potential to identify windows vulnerable to energy loss. Notably, our Cascade U-Net, which builds upon the relatively lightweight U-Net architecture, also exhibited strong performance, reinforcing the practical value of this method. Our approach offers a practical solution for enhancing energy efficiency in buildings by providing more precise segmentation results.

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APA Style
Jung, H., Park, H., Jung, H.S., Lee, K. (2024). Enhancing building facade image segmentation via object-wise processing and cascade u-net. Computers, Materials & Continua, 81(2), 2261-2279. https://doi.org/10.32604/cmc.2024.057118
Vancouver Style
Jung H, Park H, Jung HS, Lee K. Enhancing building facade image segmentation via object-wise processing and cascade u-net. Comput Mater Contin. 2024;81(2):2261-2279 https://doi.org/10.32604/cmc.2024.057118
IEEE Style
H. Jung, H. Park, H.S. Jung, and K. Lee, “Enhancing Building Facade Image Segmentation via Object-Wise Processing and Cascade U-Net,” Comput. Mater. Contin., vol. 81, no. 2, pp. 2261-2279, 2024. https://doi.org/10.32604/cmc.2024.057118



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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