Using deep learning for surface defects identification of optical components
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摘要: 精密光学元件表面疵病的人工检测分类方法效率低，且准确率易受疲劳等人工因素影响，而基于传统机器学习方法的分类准确率有待进一步提高。提出了一种基于深度学习卷积神经网络的光学大尺寸元件表面疵病识别方法。首先，通过现场实验采集并整理了大尺寸镜面疵病样本；接着，基于单通道灰度图像构建融合梯度的三通道图像，挖掘更深入的特征表达；最后，基于经典的LeNet网络，提出了面向激光惯性约束聚变(ICF)的光学元件表面疵病识别网络ICFNet，该网络不需要复杂的手工特征设计和提取，仅使用原始灰度图像就实现高效的疵病识别。实验结果表明：针对包含麻点、划痕和灰尘的三类疵病数据，ICFNet相较于使用多项特征和支持向量机的传统方法拥有较好的分类准确率。Abstract: The manual classification methods for surface defects of precision optical elements are inefficient and the accuracy is easily affected by manual factors such as fatigue. And the accuracy based on traditional machine learning methods needs to be further improved. We propose an inspection method for surface defects of large-caliber optical elements using deep learning convolutional neural network. Firstly, collect and catalog a dataset of the surface defects of large-caliber optical elements through field tests. Then, for mining deeper feature expression, creat gradient-based three channels fusion image by the single-channel grayscale image. Finally, put forward the ICFNet which aims at Inertial Confinement Fusion (ICF) based on typical LeNet. The ICFNet does not require sophisticated manual design or feature extraction, only uses grayscale image to realize efficient inspection for surface flaws of large-caliber optical elements. Experiments show that ICFNet has better classification accuracy than traditional methods using multiple features and support, vector machines for three types of defects, including scratch, dust, and pits. This method has certain application value in surface defects identification of optical components.
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图 5 不同卷积层数的训练结果
Figure 5. Training results with different number of convolution layers
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