基于可见光和红外图像融合的建筑外墙空鼓与脱落识别方法研究

王玮; 米庆仁; 肖云; 杨新聪

doi:10.13204/j.gyjzG22112305

基于可见光和红外图像融合的建筑外墙空鼓与脱落识别方法研究

doi: 10.13204/j.gyjzG22112305

王玮¹,
米庆仁²,
肖云³,
杨新聪^2, ,

1. 广州地铁集团有限公司, 广州 510220;
2. 哈尔滨工业大学(深圳), 广东深圳 518055;
3. 中铁建华南建设有限公司, 广州 510220

基金项目:

国家重点研发计划(2022YFC3801203)

国家自然科学基金青年科学基金项目(52108286)

深圳市高等院校稳定支持计划项目(GXWD20220818002513001)。

详细信息

作者简介:
王玮,高级工程师,主要从事建设工程管理、轨道交通工程数字化建造的相关研究。

通讯作者:
杨新聪,博士,助理教授,主要从事智能建造、智慧运维的相关研究,yangxincong@hit.edu.cn。

计量
- 文章访问数: 49
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-23
- 网络出版日期: 2024-06-22

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

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

摘要

摘要: 建筑外墙空鼓与脱落的识别对于确保城市老旧建筑物周围公共安全至关重要。传统的人工原位检测方法需要耗费大量人力物力且存在一定的安全风险,此外识别结果也会受到专业人员的工作经验和工作状态等主观因素的影响。近年来,采用无人机进行图像采集并通过人工智能模型对建筑外墙缺陷进行识别的方法逐渐流行开来。然而,目前对于缺陷检测的研究仅针对单一模态的可见光图像或者红外图像,往往只能对某一缺陷进行检测,且没有考虑缺陷之间的转换关系。针对这一问题,通过融合建筑外墙的可见光和红外图像,结合两种模态的图像信息,并通过不同深度的UNet和Res-UNet模型对融合后图像进行建筑外墙缺陷识别,比较了不同深度模型的识别效果。试验结果表明,深度为4的Res-UNet模型对建筑外墙的空鼓和脱落具有很好的识别效果。
- 多模态融合 /
- 可见光和红外图像融合 /
- 外墙缺陷识别 /
- 深度学习
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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参考文献(22)

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