Towards automatic analysis of ultrasonic time-of-flight diffraction data using genetic-based Inverse Hough Transform
Towards automatic analysis of ultrasonic time-of-flight diffraction data using genetic-based Inverse Hough Transform
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DOI：
10.1784/insi.2009.51.4.184
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发表时间：
2009-04
期刊：
影响因子：
1.1
通讯作者：
K. Maalmi;R. Benslimane;L. Voon;E. Fauvet
中科院分区：
文献类型：
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作者：
K. Maalmi;R. Benslimane;L. Voon;E. Fauvet
超声衍射时差法（TOFD）是一种无损检测技术，已被证明对钢结构中的埋藏裂纹缺陷的检测、定位和尺寸确定非常有效。但是，它会产生大量需要手动处理和解释的数据。这个过程既费时又费力。此外，它需要操作者的技能、警觉性和经验。因此，它容易受到人为错误的影响。为了节省时间、精力和检测成本，同时提高检测率，需要开发自动分析工具。因此，本文提出了一个应用程序的图像处理技术的超声TOFD数据的B扫描图像表示，以便利用图像表示的信息的权力。在B扫描图像中，裂纹缺陷的特征在于多个衍射弧。为了检测这些多个衍射弧，从而揭示被检结构中裂纹的存在，文献中提出了一些基于传统Hough变换（HT）的方法。与传统HT相关的主要问题是其大的数据存储需求和昂贵的计算时间。为了科普这些问题，我们提出了使用逆Hough变换（IHT）的投票过程中进行的图像空间，而不是参数空间。与IHT，在参数空间中的局部峰值检测问题被转换为一个参数优化问题，使用遗传算法来解决。建议的基于遗传的逆表决Hough变换'GBIVHT'算法允许自动检测衍射弧，因此裂纹缺陷，同时避免了传统HT的计算复杂性以及巨大的存储需求。
Ultrasonic Time-of-Flight Diffraction (TOFD) is a non-destructive inspection technique that has proved to be very effective for the detection, localisation and sizing of buried crack defects in steel structures. However, it produces a huge amount of data that are manually processed and interpreted. This process is time consuming and painstaking. Moreover, it requires the skill, alertness and experience of the operator. Consequently, it is subject to human errors. In order to save time, effort and inspection cost while at the same time increasing the detection rate, automatic analysis tools need to be developed. This paper presents thus an application of image processing techniques to the B-scan image representation of ultrasonic TOFD data so as to take advantage of the power of image representation of information. In a B-scan image, crack defects are characterised by multiple arcs of diffraction. In order to detect these multiple arcs of diffraction and thus reveal the presence of a crack in the structure under inspection, some methods based on conventional Hough Transform (HT) were proposed in the literature. The main problems related to conventional HT are its large data storage requirements and expensive computation times. To cope with these problems, we propose the use of the Inverse Hough Transform (IHT) where the voting process is performed in the image space rather than the parameter space. With the IHT, the local peak detection problem in the parameter space is converted to a parameter optimisation problem that is solved using Genetic Algorithms. The proposed Genetic-Based Inverse Voting Hough Transform 'GBIVHT' algorithm allows thus the automatic detection of the arcs of diffraction, and therefore the crack defects, while avoiding the computational complexity as well as the huge storage requirement of conventional HT.