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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Feature Concentration for Supervised and Semisupervised Learning With Unbalanced Datasets in Visual Inspection
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Feature Concentration for Supervised and Semisupervised Learning With Unbalanced Datasets in Visual Inspection

机译：在目视检查中使用不平衡数据集进行监督和半熟学习的功能集中

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摘要

The application of deep learning to visual inspection is hampered by the scarcity of images of defective components, which are rare in modern manufacturing, and by a general lack of labeled images, because labeling is expensive. In this article, we address this by introducing feature concentration, in which features from annotated images of defective and normal components are separated in feature space by moving them towards cluster centers. We also apply feature concentration to consistency regularization in semisupervised classification, in which only a small proportion of the data is annotated. Results were compared with those from existing approaches for unbalanced and semisupervised data, using images obtained during inspection of a smartphone component. In a supervised setting, average accuracy increased by around 5%, and in a semisupervised setting, the improvement varied between 7% and 11%, depending on the supervision ratio. We also applied feature concentration to more general public datasets, where it again outperformed the other methods.
机译：深入学习对视觉检查的应用受到缺陷组件图像的稀缺的缺点，这些成分在现代制造中罕见，并且通过普遍缺乏标记的图像，因为标签昂贵。在本文中，我们通过引入特征浓度来解决这一点，其中来自注释的有缺陷和正常组件的图像的特征在特征空间中通过向集群中心移动到特征空间中。我们还将特征集中应用于半培训分类中的一致性正则化，其中只有一小部分数据被注释。将结果与来自在检测期间获得的智能手机组件中获得的图像的现有方法与现有方法进行比较。在监督设定中，平均精度增加约5％，并且在半经验中的环境中，改善在7％至11％之间，取决于监督比率。我们还将功能集中应用于更多通用公共数据集，在那里它再次优于其他方法。

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