A Deep Learning Technique for Optical Inspection of Color Contact Lenses

• Published in: Applied sciences Vol. 13; no. 10; p. 5966
• Main Authors: Kim, Tae-yun, Park, Dabin, Moon, Heewon, Hwang, Suk-seung
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Accuracy; Artificial intelligence; automatic optical inspection; Cameras; Color; colored contact lens; Complexity; Contact lenses; convolutional neural network; Deep learning; Defects; Equilibrium; hydrogel; Hydrogels; Inspection; Manufacturing; Manufacturing industry; Methods; Neural networks; Polymerization; South Korea; Japan
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13105966

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.