Autors: Hristov, V. D., Pepedzhiev, D. P.
Title: Digital Image Analysis of Surface Quality in Manufactured Flat Optical Lenses
Keywords: automated quality control, image analysis, optical lens inspection, surface defect detection

Abstract: The production of optical lenses demands stringent quality control to ensure compliance with international standards, particularly ISO 10110-7 for surface defects. Traditional manual inspection methods are labor-intensive and subjective, necessitating automated solutions. This study presents a cost-effective, microscope-based imaging system for defect analysis in plano-parallel lenses, integrating a high-resolution Euromex camera, Schoelly Flexilux LED illumination, and ImageFocus Alpha software. The system achieves a resolution of 0.8 μm/pixel, enabling detection of scratches ≥1 μm and pits ≥50 μm2, while adhering to the 5% total defect area limit. Calibration with NIST-traceable standards ensures traceability, and automated workflows reduce subjectivity in defect assessment. Experimental results demonstrate successful defect identification, including chips and scratches, validated against reference etalons. The proposed system lays the groundwork for future AI-driven quality control, offering scalability for industrial applications.

References

  1. Y. Liu et al., "Machine Vision for Surface Defect Detection, " IEEE Trans. Ind. Informat., vol. 17, no. 4, pp. 2405-2424, 2021.
  2. D. Pepedzhiev and V. Hristov, "Software for image analysis and inspection of optical lens, " 2023 27th International Conference on Information Technology (IT), Zabljak, Montenegro, 2023, pp. 1-5, doi: 10.1109/IT57431.2023.10078533.
  3. M. N. Afzal et al., "Machine Vision-Based Automated Surface Defect Detection for Flat Optics Using Deep Learning, " IEEE Trans. Ind. Informat., vol. 18, no. 5, pp. 3421-3430, 2022.
  4. P. Zhang et al., "High-Resolution Optical Inspection Systems: Calibration and Uncertainty Analysis, " IEEE Instrum. Meas. Mag., vol. 25, no. 3, pp. 56-63, 2022.
  5. IEEE Standards Association, IEEE Guide for Smart Manufacturing: Optical Inspection Systems, IEEE Std 1873-2022, 2022.
  6. L. Wang et al., "Transfer Learning for Scratch Detection in Optical Components, " IEEE Access, vol. 10, pp. 45678-45689, 2022.
  7. Y.-H. Chen et al., "An image based optical lens eccentric error inspection system, " 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings, Graz, Austria, 2012, pp. 1094-1097, doi: 10.1109/I2MTC.2012.6229221.
  8. R. Gupta and S. Lee, "Optimized LED Illumination for Surface Defect Contrast Enhancement, " IEEE J. Photon., vol. 14, no. 2, pp. 1-10, 2023.

Issue

2025 14th Mediterranean Conference on Embedded Computing, MECO 2025 - Proceedings, 2025, Montenegro, https://doi.org/10.1109/MECO66322.2025.11049202

Цитирания (Citation/s):
1. Damyanov I., Saliev D., Dimitrov K., Remote Monitoring of Pasture Biomass and Vegetation Health in Free-Range Cattle Systems Using Block-Based RGB Drone Imagery Classification and the Excess Green Index, 2025, 2025 10th International Conference on Energy Efficiency and Agricultural Engineering EE and Ae 2025 Conference Proceedings, issue 0, DOI 10.1109/EEAE65901.2025.11273380 - 2026 - в издания, индексирани в Scopus

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science