Research on the Detection Method of Hollowing and Missing for Building Exterior Walls Based on Visible and Infrared Image Fusion

WANG Wei; MI Qingren; XIAO Yun; YANG Xincong

doi:10.13204/j.gyjzG22112305

Volume 54 Issue 5

May 2024

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2024 > 54(5): 51-59

YU Yang, LIU Xiaoyu, TIAN Darui, YANG Hui. STUDY ON THE INFLUENCE OF PLANNING ELEMENTS ABOUT GEOMETRIC SHAPES OF STREET CANYONS ON SOLAR POTENTIAL[J]. INDUSTRIAL CONSTRUCTION, 2020, 50(7): 137-142. doi: 10.13204/j.gyjzG19113039

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WANG Wei, MI Qingren, XIAO Yun, YANG Xincong. Research on the Detection Method of Hollowing and Missing for Building Exterior Walls Based on Visible and Infrared Image Fusion[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(5): 51-59. doi: 10.13204/j.gyjzG22112305

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Research on the Detection Method of Hollowing and Missing for Building Exterior Walls Based on Visible and Infrared Image Fusion

doi: 10.13204/j.gyjzG22112305

1. Guangzhou Metro Group Co., Ltd., Guangzhou 510220, China;
2. Harbin Institute of Technology(Shenzhen), Shenzhen 518055, China;
3. CRCC South China Construction Co., Ltd, Guangzhou 510220, China

Received Date: 2022-11-23
Available Online: 2024-06-22

Abstract

Abstract

The detection of hollowing and missing of building exterior walls is crucial to ensure the public safety around aging buildings in cities. The traditional artificial in-situ detection methods are time- and labor-consuming with safety risks. In addition, the detection results will also be affected by subjective factors such as professional experience and working status. The method of image acquisition by UAV and detection of building exterior wall defects by artificial intelligence model has become popular. However, the current research on defect detection only focuses on visible images or infrared images of a single modality, and only detect a certain defect without considering the mutual conversion between defects. To address this issue, this research combined the visible and infrared images of the building exterior wall, considered the image information from two modalities, and compared the UNet and Res-UNet models of different depths to identify the building exterior wall defects in the fused images. The experimental results showed that the Res-UNet model with a depth of 4 performed excellent on the hollowing and missing of the building exterior wall.
- multimodal fusion,
- visible and infrared image fusion,
- exterior wall defect detection,
- deep learning

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References

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