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

Area of Science:

• Ophthalmic manufacturing
• Image processing
• Quality control

Background:

• Color contact lenses require stringent quality control due to direct eye wear.
• Center deviation (CD) defects, where the colored area (CA) is off-center, are a significant quality concern.
• Accurate measurement of CA deviation is crucial for defect detection.

Purpose of the Study:

• To develop and evaluate an image processing method for detecting center deviation defects in color contact lenses.
• To quantify the accuracy of the proposed method in measuring the deviation of the colored area from the center point.

Main Methods:

• Utilized semantic segmentation to simplify lens images and minimize noise.
• Employed the Hough circle transform algorithm to precisely measure the deviation of the colored area's center.
• Applied image analysis techniques for defect detection.

Main Results:

• The proposed method effectively detects center deviation defects in color contact lenses.
• Achieved a 71.2% reduction in error compared to existing research methods.
• Demonstrated the efficacy of semantic segmentation and Hough circle transform for this application.

Conclusions:

• The developed image processing technique offers a reliable and accurate solution for identifying CD defects in color contact lens production.
• This method enhances quality control by providing a significant reduction in measurement error.
• The approach holds promise for improving the safety and quality of color contact lenses.