Abstract Recent efforts to create a smart factory have inspired research that analyzes process data collected from Internet of Things (IOT) sensors, to predict product quality in real time. This requires an automatic defect inspection system that quantifies product quality data by detecting and classifying defects in real time. In this study, we propose a vision-based defect inspection system to inspect metal surface defects. In recent years, deep convolutional neural networks (DCNNs) have been used in many manufacturing industries and have demonstrated the excellent performance as a defect classification method. A sufficient amount of training data must be acquired, to ensure high performance using a DCNN. However, owing to the nature of the metal manufacturing industry, it is difficult to obtain enough data because some defects occur rarely. Owing to this imbalanced data problem, the generalization performance of the DCNN-based classification algorithm is lowered. In this study, we propose a new convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to solve this problem. The CVAE-based data generation technology generates sufficient defect data to train the classification model. A conditional CVAE (CCVAE) is proposed to generate images for each defect type in a single CVAE model. We also propose a classifier based on a DCNN with high generalization performance using data generated from the CCVAE. In order to verify the performance of the proposed method, we performed experiments using defect images obtained from an actual metal production line. The results showed that the proposed method exhibited an excellent performance.

中文翻译：

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基于深度学习和数据增强的金属表面缺陷自动检测系统

摘要 最近创建智能工厂的努力激发了对从物联网 (IOT) 传感器收集的过程数据进行分析以实时预测产品质量的研究。这需要一个自动缺陷检测系统，通过实时检测和分类缺陷来量化产品质量数据。在这项研究中，我们提出了一种基于视觉的缺陷检测系统来检测金属表面缺陷。近年来，深度卷积神经网络 (DCNN) 已被用于许多制造行业，并已证明作为缺陷分类方法的卓越性能。必须获得足够数量的训练数据，以确保使用 DCNN 的高性能。然而，由于金属制造行业的性质，一些缺陷很少发生，因此很难获得足够的数据。由于这种不平衡的数据问题，降低了基于 DCNN 的分类算法的泛化性能。在这项研究中，我们提出了一种新的卷积变分自编码器（CVAE）和基于深度 CNN 的缺陷分类算法来解决这个问题。基于 CVAE 的数据生成技术生成足够的缺陷数据来训练分类模型。提出了条件 CVAE (CCVAE) 来为单个 CVAE 模型中的每种缺陷类型生成图像。我们还提出了一种基于 DCNN 的分类器，该分类器使用从 CCVAE 生成的数据具有高泛化性能。为了验证所提出方法的性能，我们使用从实际金属生产线上获得的缺陷图像进行了实验。结果表明，所提出的方法表现出优异的性能。