赵迪, 徐志胜. 基于MRSVD红外热像融合的混凝土结构火灾损伤检测方法[J]. 信息与控制, 2017, 46(1): 19-24, 40. DOI: 10.13976/j.cnki.xk.2017.0019
引用本文: 赵迪, 徐志胜. 基于MRSVD红外热像融合的混凝土结构火灾损伤检测方法[J]. 信息与控制, 2017, 46(1): 19-24, 40. DOI: 10.13976/j.cnki.xk.2017.0019
ZHAO Di, XU Zhisheng. Detection of Fire Damage to Concrete Structures with Infrared Thermal Fusion Based on Multi-resolution Singular Value Decomposition[J]. INFORMATION AND CONTROL, 2017, 46(1): 19-24, 40. DOI: 10.13976/j.cnki.xk.2017.0019
Citation: ZHAO Di, XU Zhisheng. Detection of Fire Damage to Concrete Structures with Infrared Thermal Fusion Based on Multi-resolution Singular Value Decomposition[J]. INFORMATION AND CONTROL, 2017, 46(1): 19-24, 40. DOI: 10.13976/j.cnki.xk.2017.0019

基于MRSVD红外热像融合的混凝土结构火灾损伤检测方法

Detection of Fire Damage to Concrete Structures with Infrared Thermal Fusion Based on Multi-resolution Singular Value Decomposition

  • 摘要: 利用红外热像技术对混凝土结构火灾损伤进行检测,容易受到多种环境因素干扰,其成像效果不稳定,图像信噪比低、对比度低,因此单一图像难以准确反映混凝土内部损伤.针对这一问题,本文提出了一种基于多分辨率奇异值分解(MRSVD)的图像融合方法.该方法对现有的奇异值分解算法进行二分递推改进,从而将多幅混凝土红外热像分解为不同分辨率下的近似分量和多个细节分量;采用不同的策略将不同分量进行融合,并重构新的红外热像;在此基础上采用模拟退火算法对重构后的图像进行增强处理,提高图像的清晰度和辨识率.实验结果表明为:相比于传统方法,本文算法在清晰度、空间分辨率、均方根误差以及峰值信噪比等4项指标上具有较大的优势,从而为混凝土火灾损伤的损伤检测提供了可靠依据.

     

    Abstract: When using an infrared thermograph to detect fire damage to concrete structures, interference comes from a variety of environmental factors, causing an unstable imaging effect, low SNR, and low contrast. Therefore, a single image cannot accurately reflect the internal defects in the concrete. To solve this problem, this paper proposes an image fusion method based on multiresolution singular values. The existing singular value analysis algorithm for multiresolution improvement is recursive, resulting in different resolutions. The components of the multiple infrared thermograph were divided into concrete and a variety of detailed components. For different components, we used different strategies of image fusion as well as the new infrared thermograph. This was to study the annealing algorithm, enhance the image processing, and improve the image clarity and identification rate. Experimental results show that this algorithm shows improvement in definition, spatial resolution, root mean square error, and peak signal-to-noise ratio and provides a reliable basis for the nondestructive testing of fire-damaged concrete.

     

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