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Fourier-Transform-Based Method for Automated Steel Bridge Coating Defect Recognition

机译:基于傅立叶变换的自动化钢桥涂层缺陷识别方法

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摘要

Intelligent computerized system can analyze and process vast amounts of data instantaneously in the civil engineering domain recently. However, most of previously developed image assessment methods for steel bridge rust inspection could not directly deal with acquired images. The acquired images should be firstly classified into two groups, non-defect or defect, by naked eyes or using the method reported in Lee's PhD dissertation (Lee 2005). Lee's method works effectively with blue-coated steel bridge images in Indiana. However, its effect on other colors or in particular environmental conditions, such as non-uniform illumination, has not yet been explored. A defect recognition method for steel bridge surface was proposed by using Fourier Transform and color image processing method in order to adapt to various background colors and overcome the influences from particular environmental conditions, such as non-uniform illumination. Fourier Transform in this system is aimed to determine the existence of defects in an acquired image. The proposed method shows better performance than previous methods such as rust defect recognition method (RUDR) proposed by Lee in 2005.
机译:智能计算机化系统最近可以在土木工程领域瞬间分析和处理大量数据。然而,最先前发达的钢桥防锈检查的图像评估方法无法直接处理获得的图像。所获取的图像应首先将肉眼或使用李博士论文报告的方法分为两组,非缺陷或缺陷,或者使用李博士论文(LEE 2005)。 Lee的方法有效地与印第安纳州的蓝色涂层钢桥图像有效地工作。然而,它对其他颜色或特别是环境条件(例如非均匀照明)的影响尚未探讨。通过使用傅立叶变换和彩色图像处理方法提出了一种缺陷识别方法,以适应各种背景颜色并克服来自特定环境条件的影响,例如不均匀的照明。该系统中的傅里叶变换旨在确定所获取的图像中的缺陷存在。该方法表现出比以前的方法更好的性能,例如李在2005年提出的锈病缺陷识别方法(RUDR)。

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