The typical defect detection method is based on the use of conventional cameras with limited dynamic range and image processing algorithm. Their identification efficiency is low particularly for industrial application because the light intensity from some highly reflective surfaces can easily exceed the maximum intensity limit of the sensor. In this paper, we take advantage of the fast spatial light modulation characteristics of the DMD system. By generating highlight defect datasets and training the neural network (i.e., mask region-based convolutional neural network), the defects covered by strong light can be detected quickly and automatically.

中文翻译：

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基于数字微镜器件的高反射表面缺陷快速检测

典型的缺陷检测方法基于使用具有有限动态范围和图像处理算法的传统相机。它们的识别效率很低，特别是在工业应用中，因为来自一些高反射表面的光强度很容易超过传感器的最大强度限制。在本文中，我们利用了 DMD 系统的快速空间光调制特性。通过生成高亮缺陷数据集并训练神经网络（即基于掩膜区域的卷积神经网络），可以快速自动检测强光覆盖的缺陷。