CLensRimVision: A Novel Computer Vision Algorithm for Detecting Rim Defects in Contact Lenses

Area of Science:

• Ophthalmic manufacturing
• Computer vision applications
• Quality control methodologies

Background:

• Automated optical inspection (AOI) is crucial for contact lens safety and integrity.
• Traditional methods struggle with subtle, irregular rim defects.
• Computer vision's role in defect detection is expanding.

Purpose of the Study:

• To develop and evaluate a novel algorithm for automated detection of contact lens rim defects.
• To compare the effectiveness of computer vision-based defect detection against traditional methods.
• To enhance the precision and reliability of contact lens quality control.

Main Methods:

• Proposed CLensRimVision algorithm integrating image preprocessing, circle detection, and polar coordinate transformation.
• Defect detection based on adaptable thickness- or area-based criteria.
• Automated visualization of detected defects on contact lens rims.

Main Results:

• Achieved an exemplary Average Precision (AP) score of 0.937.
• Demonstrated high performance in identifying subtle and irregular rim defects.
• Validated the algorithm's effectiveness for diverse contact lens characteristics.

Conclusions:

• The CLensRimVision algorithm provides a precise and automated solution for contact lens rim defect detection.
• This computer vision approach offers superior performance compared to traditional methods.
• Results guide manufacturers and researchers in optimizing contact lens quality assurance.