摘要翻译：
混凝土结构缺陷的无损检测(NDD)已有几十年的发展历史。虽然报告的成功有限，但仍然存在重大限制。其主要局限性在于环境因素如云、影、水、表面纹理等造成的高信噪比，决策仍依赖于对图像内容判读的工程判断。对主成分热图法等时间序列方法进行了实验，得到了一些改进的结果。利用机器学习方法进行图像处理的最新进展，使得对缺陷热特征的定量检测成为可能。本文给出了一个用主成分分析在时域内表示热工特征的过程，并用两种监督学习模型对探测预测进行了回归。三个独立的实验在类似的实验室设置中进行，但在不同的条件下，以说明模型的性能和推广。结果表明，通过对参数的适当调整，该方法对检测目的是有效的。未来的研究将侧重于实现更复杂的结构化模型，以处理自然条件下更现实的案例。
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英文标题：
《Detecting Concrete Abnormality Using Time-series Thermal Imaging and
  Supervised Learning》
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作者：
Chongsheng Cheng and Zhigang Shen
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最新提交年份：
2019
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Nondestructive detecting defects (NDD) in concrete structures have been explored for decades. Although limited successes were reported, major limitations still exist. The major limitations are the high noises to signal ratio created from the environmental factors, such as cloud, shadow, water, surface texture etc. and the decision making still relies on the engineering judgment of interpretation of image content. Time-series approach, such as principle component thermography approach has been experimented with some improved results. Recent progress in image processing using machine learning approach made it possible for detecting defects thermal features in more quantitative ways. In this paper, we provide a procedure to represent the thermal feature in the time domain by principal component analysis and regress the prediction of detection by two schemes of supervised learning models. Three independent experiments were conducted in a similar laboratory setup but varied in conditions to illustrate the performance and generalization of models. Results showed the effectiveness for the detection purpose with appropriate tuning for parameters. Future studies will focus on implementing more sophisticated structured models to handle more realistic cases under natural conditions.
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PDF链接：
https://arxiv.org/pdf/1804.05406