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Surface inspection system for industrial components based on shape from shading minimization approach

机译：基于阴影最小化方法的形状的工业零件表面检测系统

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摘要

An inspection system using estimated three-dimensional (3-D) surface characteristics information to detect and classify the faults to increase the quality control on the frequently used industrial components is proposed. Shape from shading (SFS) is one of the basic and classic 3-D shape recovery problems in computer vision. In our application, we developed a system using Frankot and Chellappa SFS method based on the minimization of the selected basis function. First, the specialized image acquisition system captured the images of the component. To eliminate noise, wavelet transform is applied to the taken images. Then, estimated gradients were used to obtain depth and surface profiles. Depth information was used to determine and classify the surface defects. Also, a comparison made with some linearization-based SFS algorithms was discussed. The developed system was applied to real products and the results indicated that using SFS approaches is useful and various types of defects can easily be detected in a short period of time.
机译：提出了一种使用估计的三维（3-D）表面特征信息检测和分类故障以提高对常用工业组件的质量控制的检查系统。阴影形状（SFS）是计算机视觉中基本且经典的3-D形状恢复问题之一。在我们的应用程序中，我们基于所选基函数的最小化，使用Frankot和Chellappa SFS方法开发了一个系统。首先，专用图像采集系统捕获了组件的图像。为了消除噪声，将小波变换应用于所拍摄的图像。然后，使用估计的梯度来获得深度和表面轮廓。深度信息用于确定和分类表面缺陷。此外，还讨论了与一些基于线性化的SFS算法的比较。该开发的系统应用于实际产品，结果表明使用SFS方法是有用的，并且可以在短时间内轻松检测到各种类型的缺陷。

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