Fresnel lens defect classification using deep learning technique
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 im...
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| Format: | Conference or Workshop Item |
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2022
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| _version_ | 1796866753543798784 |
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| author | Loo, Kean Li Chong, Keat Saw Ibrahim, M. H. |
| author_facet | Loo, Kean Li Chong, Keat Saw Ibrahim, M. H. |
| author_sort | Loo, Kean Li |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T21:16:54Z |
| format | Conference or Workshop Item |
| id | utm.eprints-99420 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:16:54Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | utm.eprints-994202023-02-27T04:02:26Z http://eprints.utm.my/99420/ Fresnel lens defect classification using deep learning technique Loo, Kean Li Chong, Keat Saw Ibrahim, M. H. TK Electrical engineering. Electronics Nuclear engineering 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. 2022 Conference or Workshop Item PeerReviewed Loo, Kean Li and Chong, Keat Saw and Ibrahim, M. H. (2022) Fresnel lens defect classification using deep learning technique. In: 8th International Conference on Computational Science and Technology, ICCST 2021, 28 - 29 August 2021, Virtual, Online. http://dx.doi.org/10.1007/978-981-16-8515-6_42 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Loo, Kean Li Chong, Keat Saw Ibrahim, M. H. Fresnel lens defect classification using deep learning technique |
| title | Fresnel lens defect classification using deep learning technique |
| title_full | Fresnel lens defect classification using deep learning technique |
| title_fullStr | Fresnel lens defect classification using deep learning technique |
| title_full_unstemmed | Fresnel lens defect classification using deep learning technique |
| title_short | Fresnel lens defect classification using deep learning technique |
| title_sort | fresnel lens defect classification using deep learning technique |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT lookeanli fresnellensdefectclassificationusingdeeplearningtechnique AT chongkeatsaw fresnellensdefectclassificationusingdeeplearningtechnique AT ibrahimmh fresnellensdefectclassificationusingdeeplearningtechnique |