| dc.description.abstract | The manufacturing industry is the economic foundation of Taiwan. The development of every technology is so impressive in the 21st century as we should see. Even though the performance of product lines is getting improved, the yield rate of products is still the main issue. The mission of defect detection among products would cost significant labor sources and work time. The products with defects would cause the manufacturers or consumers further losses. Those products would probably result in injuries or even deaths in some situations. In traditional manufacturing, employers would like to employ a large amount of labor intervention to ensure the quality of products. However, because employees′ energy is not always stable, it may cause the outflow of defective products, resulting in significant losses. High-tech industries currently use AOI (Automated Optical Inspection) to detect defects in products. AOI is achieved by high-speed and high-precision optical instruments, along with artificial intelligence of machine vision technology. In this research, we used the YOLO technique to develop a product detection method. The YOLO framework uses convolutional neural networks to learn how to identify defective products. The convolutional kernels deal with input images, and then the fully connected layer outputs the predicted out classified results. The AOI technique technically relies on the pre-set parameters for identification, whereas YOLO uses a well-trained model obtained by learning from data. The YOLO technique needs a large number of data to train its model, and then the well-trained model can continuously improve the ability of identification through sustained learnings. That is the reason that participants believe the YOLO technique outperforms the AOI. In conclusion, it is easier for YOLO to show its strengths in massive production lines, while AOI is suitable for smaller production lines. The purpose of this research thesis is to develop a defect detection method by utilizing the YOLO technique. Hopefully, we can extend the developed model to applications on medical products. | en_US |