| Summary: | 碩士 === 國立臺灣科技大學 === 機械工程系 === 107 === In Taiwan, pharmaceutical industries generally inspect surface of tablets for defects manually. This will result in not only time-consuming but also undesirable misjudgments. In recent years, due to the fast development of deep learning, Neural Network has been applied to more and more fields. In order to train the Convolutional Neural Networks for the usage of defects detection, a large number of defective samples have to be provided. However, it is very difficult to collect enough defective samples, and it also takes enormous amount of time to mark the defects manually.
This research makes use of Generative Adversarial Network(GAN) to train the neural network model by only providing images of normal tablets. At the same time, Wasserstein Generative Adversarial Network(WGAN) and Autoencoder are used to rebuild a GAN for image reconstruction, comparing the image before and after reconstruction to detect the defects. Because of GAN fails to detect small defect area, this research also implements traditional optical inspection techniques to inspect the defect of black spots. A series of experiments proves that the algorithms developed in this thesis is able to give high defect inspection rate.
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