Loo, Kean Li and Saw, Chong Keat and Ibrahim, M. H. (2022) Fresnel lens defect classification using deep learning technique. In: Proceedings of the 8th International Conference on Computational Science and Technology ICCST 2021, Labuan, Malaysia, 28–29 August. Lecture Notes in Electrical Engineering, 835 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 547-561. ISBN 978-981168514-9
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Official URL: http://dx.doi.org/10.1007/978-981-16-8515-6_42
Abstract
Plastic injection molded Fresnel lens is one of the important components for illumination in smart devices. To perform inspection on this type of optical component is challenging for machine vision due to the presence of groove pattern and texture. This paper discusses the limitation of classical image analysis for defect inspection and proposes a Deep Convolutional Neural Network (CNN) with Transfer Learning for defect classification. This paper also presents a Hybrid CycleGAN and geometric augmentation to expand image dataset for model training.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | deep learning, defect classification, fresnel lens, image augmentation, image generation |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering - School of Electrical |
| ID Code: | 100489 |
| Deposited By: | Yanti Mohd Shah |
| Deposited On: | 14 Apr 2023 02:13 |
| Last Modified: | 14 Apr 2023 02:13 |
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