大口径精密表面疵病的数字化检测系统
Digital detection system of surface defects for large aperture optical elements
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摘要: 根据散射光成像原理，采用大小两个视场来获取不同精度的暗背景下的亮疵病图像，设计了完整的数字化表面疵病检测系统。该系统采用多区域自适应阈值分割算法对图像进行分割，然后采用基于等价归并标记方法快速提取疵病的特征参数，最后利用BP神经网络对疵病进行分类。实验结果表明该方法既满足实时性需求，又取得了较好的分类检测效果。Abstract: Based on the light defect images against the dark background in a scattering imaging system, a digital detection system of surface defects for large aperture optical elements has been presented. In the system, the image is segmented by a multi-area self-adaptive threshold segmentation method, then a pixel labeling method based on replacing arrays is adopted to extract defect features quickly, and at last the defects are classified through back-propagation neural networks. Experiment results show that the system can achieve real-time detection and classification.
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Key words:
- icf /
- defect /
- fast labeling algorithm /
- feature parameter /
- classification rule
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