| Summary: | 碩士 === 亞洲大學 === 資訊工程學系 === 107 === There are many production lines of traditional industrial factories in our country, which are applied to automated optical inspection (AOI). AOI uses optical principle and mechanical vision technology to detect whether the objects on the production line are defective or flawed. It can replace the shortcomings of slow speed and easy misjudgment that using optical instruments by human, and replace the current manual visual inspection operation, improving the detection accuracy of the current manual detection, improving the detection speed and reducing the false positive rate. The ultimate goal is to achieve the ideal goal of reducing labor costs and maintaining product quality. However, AOI has no way to classify it, only to judge whether there is any flaw.
In recent years,due to the rapid development of deep learning, many deep learning models have been successfully applied in image classification.We combine deep learning with AOI and use image classification to classify AOI images to compensate for the shortcomings of traditional AOI. Disadvantages, the handling of the parts can be repaired and repaired by different defects, which further increases the production capacity. Identifying defects in objects and repairing different defects, further increasing productivity. The main focus of this study is to use different convolutional neural network models to model and predict automatic optical detection images to determine the classification of defects. Enhance the effectiveness of AOI interpretation through data science and compare the pros and cons of different models with the accuracy of their predictions.
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