Improvements of Classification Accuracy of Film Defects by Using GPU-accelerated Image Processing and Machine Learning Frameworks
Improvements of Classification Accuracy of Film Defects by Using GPU-accelerated Image Processing and Machine Learning Frameworks
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DOI：
10.1109/nicoint.2016.15
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
H. Ando;Yuki Niitsu;Masaki Hirasawa;Hiroaki Teduka;Masao Yajima
中科院分区：
文献类型：
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作者：
H. Ando;Yuki Niitsu;Masaki Hirasawa;Hiroaki Teduka;Masao Yajima
针对自然图像的图像分类研究相当活跃，利用深度学习技术取得的最新成果非常显著。另一方面，工业产品缺陷的图像分类技术大多保密，部分原因是缺陷图像包含有关产品和机密制造技术的非常敏感的信息。在一家薄膜缺陷视觉检测领域的领先企业的帮助下，我们研究了使用机器学习技术对缺陷图像进行分类的有效性。我们还利用GPU来加速辅助缺陷检测的图像处理和机器学习过程。我们提出将深度神经网络与随机森林分类器相结合用于薄膜缺陷的图像分类，这种方法比单独使用这两种技术中的任何一种效果都更好。
Research on image classification for natural images are quite actively worked on and recent achievements using deep learning techniques are tremendous. On the other hand, image classification techniques of defects in industrial products are mostly kept secret, partly because defective images contain very sensitive information about the products and the confidential manufacturing technologies. With the help of a leading company in a visual inspection of film defects, we investigated the effectivity of using machine learning techniques for classification of defect images. We also made use of GPU to accelerate both image processing to assist detection of defects and machine learning. We propose the combination of deep neural networks with random forest classifier for image classification of film defects, which performed better than using either of the two techniques alone.