A Deep Learning Technique for Optical Inspection of Color Contact Lenses
Colored contact lenses have gained popularity in recent years. However, their production process is plagued by low efficiency, which is attributed to the complex nature of the lens color patterns. The manufacturing process involves multiple complex steps that can introduce defects or inconsistencies...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/10/5966 |
| _version_ | 1797601284002414592 |
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| author | Tae-yun Kim Dabin Park Heewon Moon Suk-seung Hwang |
| author_facet | Tae-yun Kim Dabin Park Heewon Moon Suk-seung Hwang |
| author_sort | Tae-yun Kim |
| collection | DOAJ |
| description | Colored contact lenses have gained popularity in recent years. However, their production process is plagued by low efficiency, which is attributed to the complex nature of the lens color patterns. The manufacturing process involves multiple complex steps that can introduce defects or inconsistencies into the contact lenses. Moreover, manual inspection of a considerable number of contact lenses that are produced inefficiently in terms of consistency and quality by humans is prevalent. Alternatively, automatic optical inspection (AOI) systems have been developed to perform quality-control checks on colored contact lenses. However, their accuracy is limited due to the increasing complexity of the lens color patterns. To address these issues, convolutional neural networks have been used to detect and classify defects in colored contact lenses. This study aims to provide a comprehensive guide for AOI systems using artificial intelligence in the colored contact lens manufacturing process, including the benefits and challenges of using these systems. Further, future research directions to achieve a classification accuracy of >95%, which is the human recognition rate, are explored. |
| first_indexed | 2024-03-11T03:58:58Z |
| format | Article |
| id | doaj.art-bd15c704314f4c3ea86da89b950b5d17 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:58:58Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bd15c704314f4c3ea86da89b950b5d172023-11-18T00:18:35ZengMDPI AGApplied Sciences2076-34172023-05-011310596610.3390/app13105966A Deep Learning Technique for Optical Inspection of Color Contact LensesTae-yun Kim0Dabin Park1Heewon Moon2Suk-seung Hwang3Institute of AI Convergence, Chosun University, Gwangju 61452, Republic of KoreaJckmedical Co., Ltd., Gwangju 61008, Republic of KoreaJckmedical Co., Ltd., Gwangju 61008, Republic of KoreaInterdisciplinary Program in IT-Bio Convergence System, School of Electronic Engineering, Chosun University, Gwangju 61452, Republic of KoreaColored contact lenses have gained popularity in recent years. However, their production process is plagued by low efficiency, which is attributed to the complex nature of the lens color patterns. The manufacturing process involves multiple complex steps that can introduce defects or inconsistencies into the contact lenses. Moreover, manual inspection of a considerable number of contact lenses that are produced inefficiently in terms of consistency and quality by humans is prevalent. Alternatively, automatic optical inspection (AOI) systems have been developed to perform quality-control checks on colored contact lenses. However, their accuracy is limited due to the increasing complexity of the lens color patterns. To address these issues, convolutional neural networks have been used to detect and classify defects in colored contact lenses. This study aims to provide a comprehensive guide for AOI systems using artificial intelligence in the colored contact lens manufacturing process, including the benefits and challenges of using these systems. Further, future research directions to achieve a classification accuracy of >95%, which is the human recognition rate, are explored.https://www.mdpi.com/2076-3417/13/10/5966colored contact lenshydrogelautomatic optical inspectionconvolutional neural network |
| spellingShingle | Tae-yun Kim Dabin Park Heewon Moon Suk-seung Hwang A Deep Learning Technique for Optical Inspection of Color Contact Lenses Applied Sciences colored contact lens hydrogel automatic optical inspection convolutional neural network |
| title | A Deep Learning Technique for Optical Inspection of Color Contact Lenses |
| title_full | A Deep Learning Technique for Optical Inspection of Color Contact Lenses |
| title_fullStr | A Deep Learning Technique for Optical Inspection of Color Contact Lenses |
| title_full_unstemmed | A Deep Learning Technique for Optical Inspection of Color Contact Lenses |
| title_short | A Deep Learning Technique for Optical Inspection of Color Contact Lenses |
| title_sort | deep learning technique for optical inspection of color contact lenses |
| topic | colored contact lens hydrogel automatic optical inspection convolutional neural network |
| url | https://www.mdpi.com/2076-3417/13/10/5966 |
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