Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea.
Department of Architectural Engineering, Daegu Catholic University, Hayang-ro 13-13, Hayang-eup, Gyeongasan-si 38430, Korea.
Sensors (Basel). 2021 Nov 7;21(21):7396. doi: 10.3390/s21217396.
With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure's existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object's information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.
随着对结构健康监测系统应用需求的不断增长，数据成像成为进行定期例行维护检查的理想方法。通过记录和分析外部损坏，图像分析可以为结构现有基础设施的健康状况提供宝贵的信息。因此，需要有一种可靠和稳健的自动方法来报告图像上的缺陷。本文提出了一种用于图像的多元分析方法，特别是用于评估重大损坏（如裂缝）。图像分析提供了与图像相关的图形表示，如图形直方图。此外，还实现了灰度等图像处理技术，增强了图像中存在的目标信息。此外，本研究使用图像分割和神经网络，分别将图像转换为易于分析的分类器。最初，对每个混凝土结构图像进行预处理以突出显示裂缝。神经网络用于计算和分类每个区域的视觉特征，并显示出 98%的分类准确性。实验结果表明，热图像提取产生了更好的直方图和累积分布函数特征。该系统可以促进各种热图像应用的发展，例如非物理视觉识别和故障检测分析。
