A Deep Learning Technique for Optical Inspection of Color Contact Lenses

Abstract

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.

Center Deviation Measurement of Color Contact Lenses Based on a Deep Learning Model and Hough Circle Transform

“…Kim et al [ 12 ] and Kim et al [ 19 ] discussed the defect detection in color contact lenses from the injection molding process. However, these studies focused only on classifying the presence of defects, not on measuring the deviation distance of the center point.…”

An Assessment of Human Inspection and Deep Learning for Defect Identification in Floral Wreaths

“…Despite the common finding of DL algorithms outperforming human visual inspection [89,90], this is not always the case. Some studies reported mixed and similar results [83,91] or a lower model performance compared with human inspectors [92]. This variety of results indicates a gap in fully automated inspection and a prevalence of human intervention, including cases where algorithms initiate the inspection and inspectors intervene for dubious items or items below a predefined threshold [93,94].…”