Abstract
The glass lens production process requires cutting, grinding and polishing, it is easy to cause lens defects, and the production quality needs to be maintained through inspection. In the past, people relied on manual methods to detect the defects, but it causes inconsistent quality standards and costs a lot of manpower to carry out this operation. In this paper, we developed a proper image acquisition system and combine deep learning techniques and image processing algorithms to implement stringent inspection criteria based on different inspection specification requirements. Finally, a defect detection and classification system for glass lenses was successfully implemented with a defect recognition accuracy of over 95% by comparison and verification.
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Acknowledgement
The authors wish to express their appreciation for the financial support of the National Science Council of Taiwan under project 111-2221-E-224-051-
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Wu, HH., Lin, CY., Huang, SC. (2024). Design and Implementation of Optical Lens Defect Detection and Classification System. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2075. Springer, Singapore. https://doi.org/10.1007/978-981-97-1714-9_2
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DOI: https://doi.org/10.1007/978-981-97-1714-9_2
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