基于时序信息的红外图像缺陷信息提取

王东升; 王海龙; 张芳; 韩林芳; 赵怡琳

基于时序信息的红外图像缺陷信息提取

王东升^1,,
王海龙^{1, 2},
张芳^{1, 3, ,},
韩林芳^{1, 3},
赵怡琳^{1, 3}

1.
中国矿业大学(北京)力学与建筑工程学院, 北京 100083
2.
河北省土木工程诊断、改造与抗灾重点实验室, 河北张家口, 075000
3.
深部岩土力学与地下工程重点实验室, 北京 100083

基金项目:

国家自然科学基金项目 51878242

详细信息

作者简介:
王东升（1992-），男，博士研究生，研究方向为图像处理、深度学习。E-mail: wds0301@outlook.com

通讯作者:
张芳（1976-），女，博士，副教授，研究方向为深度学习、软岩水理作用，近红外光谱分析等。E-mail: zhangf76@126.com

中图分类号: TN219
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- 文章访问数: 224
- HTML全文浏览量: 50
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- 被引次数: 0
出版历程
- 收稿日期: 2021-07-12
- 修回日期: 2021-08-22
- 刊出日期: 2022-06-20

Infrared Image Defect Information Extraction Based on Temporal Information

WANG Dongsheng^1
,,
WANG Hailong^{1, 2},
ZHANG Fang^{1, 3
, ,},
HAN Linfang^{1, 3},
ZHAO Yilin^{1, 3}

1.
School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China
2.
Hebei Key Laboratory of Diagnosis, Reconstruction and Anti-disaster of Civil Engineering, Zhangjiakou 075000, China
3.
State Key Laboratory for Deep Geomechanics and Underground Engineering, China University of Mining and Technology, Beijing 100083, China

摘要

摘要: 主动红外热像检测技术中，红外图像的缺陷信息提取是其核心内容。传统的红外图像处理方法在一定程度上可以消除噪声、提高图像的对比度，但是仍存在一些问题，如：需要手动选择特征信息丰富的红外图像，红外图像增强和图像分割过程中会引入主观成分，仅仅分析单张红外图像可能存在信息丢失等问题。针对上述问题，本文根据主动红外热成像的数据特征提出了一种基于时序信息的红外图像缺陷信息提取方法。首先，通过室内实验制作含缺陷分层的混凝土试块；然后，利用主动红外热像检测技术进行三维红外图像数据的采集，提取每个像素点的时序信息；最后，采用基于时序信息的K-means方法进行缺陷特征提取。结果表明，基于时序信息的缺陷提取方法是可行的，其可以提取到隐藏的分层缺陷信息，提取效果优于基于空域信息的K-means方法。
- 主动红外热像检测技术 /
- 混凝土 /
- 时序信息 /
- 图像分割
Abstract: In active infrared thermography technology, the extraction of defect information from infrared images is crucial. Traditional image processing methods can eliminate noise and improve image contrast, but several challenges remain, such as selecting the infrared image manually, subjectivity in the process of infrared image enhancement and segmentation, and information loss in the process of a single infrared image. To overcome these challenges, this study proposes a method for extracting defect information from infrared images based on time sequence information. First, concrete blocks with delamination are fabricated by indoor experiments. Then, active infrared thermal image detection technology is used to collect the infrared image data and temporal information is extracted for each pixel. Finally, the K-means method is used for defect feature extraction based on temporal information. The results show that the defect extraction method based on temporal information can extract hidden defect information. Furthermore, its hierarchical defect information extraction effect is better than that of the K-means method based on the spatial domain.
- active infrared thermography technology /
- concrete /
- temporal information /
- image segmentation

HTML全文

图 1 主动红外热成像采集的数据结构：(a) 三维红外数据；(b) 像素点温度特征曲线

Figure 1. Active infrared thermal imaging acquisition of data structures (a) Three-dimensional infrared data; (b) Temperature characteristic curves of pixels

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图 2 实验试块和仪器：(a) 混凝土试块；(b) 红外热像仪；(c) 红外加热板

Figure 2. Test blocks and instruments: (a) Concrete test block; (b) Infrared thermal imager; (c) Infrared heating plate

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图 3 主动红外热像采集系统

Figure 3. Active infrared thermography acquisition system

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图 4 混凝土试块红外图像数据

Figure 4. Infrared image data of concrete block

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图 5 处理后的红外图像

Figure 5. Processed infrared image

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图 6 处理结果对比分析：(a) 红外原图；(b) 基于时序K-means方法；(c) 基于空域K-means方法

Figure 6. Comparative analysis of processing results: (a) Original infrared image; (b) K-means method based on temporal information; (c) K-means method based on spatial information

下载: 全尺寸图片幻灯片

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