| dc.description.abstract | With the rapid development of deep learning, how to combine industrial manufacture with deep learning in order for developing smart factories has become one of the most recent trends. For the manufacturing industry, how to improve the yield rate of the production line is the core issue. In addition to improving the production techniques, the accuracy of the product defect inspection is a crucial issue to ensure the quality and efficiency of the production line. Automated optical inspection (AOI) is a technique in computer vision that combines image processing and automatic control techniques. In contrast to the traditional way of using optical instruments for product inspection by humans, the AOI techniques can lower the labor cost and shorten the inspection time. Although the current AOI detection techniques have been widely used in production lines, in practical cases, there are still many mis-classified samples which need to be double checked by humans. The reason is that the traditional AOI technique typically applies a hard threshold on extracted features as decision criterions which is not flexible enough to deal with practical manufacturing situations and therefore results in misclassification, which, in turn, increases the cost of the quality inspection.
Defective product confirmation by human labor is expensive, with low efficiency, and mis-classification may happen again in this stage due to eye fatigue. To solve this problem, we propose to use deep learning for the AOI problem. However, deep learning is faced with problems such as unbalanced sample size and unknown types of defective samples in industrial inspection, which causes difficulties in developing algorithms. In this thesis, we propose an optimization method based on the semi-supervised deep learning method: GANomaly. GANomaly is a method of using a generative adversarial network to solve anomaly detection problems, but its accuracy and missed detection rates have not yet reached the standards suitable for manufactural production lines. Therefore, we propose research directions such as color space transformation, the rethinking of the loss functions, and modifying the anomaly score to improve accuracy. Finally, our optimized method can achieve high accuracy and low false-positive rate and outperforms baseline methods like GANomaly and AnoGAN on our dataset. | en_US |