The paper presents a fast method of fully automatic localization and classification of defects in aluminium castings based on computed microtomography images. In the light of current research and based on available publications, where such analysis is made on the basis of images obtained from standard radiography (x-ray), this is a new approach which uses microtomographic images (\(\mu \)-CT). In addition, the above-mentioned solutions most often analyze a pre-separated portion of an image, which requires the initial operator interference. The authors’ own pre-processing methods, which allow to separate the element area and potential defect areas from \(\mu \)-CT images, and methods of extraction of selected features describing these areas have been proposed in the solution discussed here. A neural network trained using the Levenberg–Marquardt method with error backpropagation has been used as a classifier. The optimal network structure 20–4–1 and a set of 20 features describing the analysed areas have been determined as a result of performed tests. The applied solutions have provided 89% correct detection for any defect size and 96.73% for large defects, which is comparable to the results obtained from methods using x-ray images. This has confirmed that it is possible to use \(\mu \)-CT images in automatic defect localization in 3D. Thanks to this method, quantitative analysis of aluminium castings can be carried out without user interaction and fully automated.

中文翻译：

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显微断层图像在自动缺陷定位和检测中的分析

本文介绍了一种基于计算机显微断层图像的全自动定位和缺陷分类的快速方法。根据当前的研究并根据现有的出版物，其中，这些分析是基于从标准射线照相（x射线）获得的图像进行的，这是一种使用显微断层图像（\（\ mu \）- CT ）。另外，上述解决方案最经常分析图像的预先分离的部分，这需要初始操作者的干预。作者自己的预处理方法，可以将元素区域和潜在的缺陷区域与\（\ mu \）分开-CT图像以及描述这些区域的选定特征的提取方法已在此处讨论的解决方案中提出。使用带有误差反向传播的Levenberg-Marquardt方法训练的神经网络已用作分类器。作为执行测试的结果，已经确定了最佳网络结构20–4-1和一组描述分析区域的20个特征。所应用的解决方案对任何缺陷尺寸提供了89％的正确检测，对于大缺陷提供了96.73％的正确检测，这与使用X射线图像的方法获得的结果相当。这已经确认可以在3D自动缺陷定位中使用\（\ mu \）- CT图像。借助这种方法，无需用户干预即可实现铝铸件的定量分析，并且可以实现全自动。