International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Based Optical Lenses Defect Detection System |
|---|---|
| ABSTRACT | Introduction Artificial intelligence (AI) technology has be found to be a potent solution for enhancing quality control systems in the manufacturing industry. This paper seeks to discuss an intelligent lens defect detection algorithm for detection defects on optical lenses. Manual inspection techniques have proven to be cumbersome, error-prone, and unreliable. The system is intended to base on computer vision and uses deep-learning to the automatic detection of defects like scratches, bubbles, cracks, and distortion in the optical lens. This includes obtaining images at high resolution of the lenses and then analysing those with convolutional neural networks (CNN). This system is meant to according to computer vision technology and employs deep learning for defect detecting including scratching, bubbles, cracks, and distortion of optical lenses. In this regard, imaging of the optical lenses with high resolutions is carried out, after which image processing is done with Convolution Neural Networks (CNN). Experimental results indicate that the AI-based approach significantly enhances accuracy, consistency, and speed compared to traditional inspection methods. |
| AUTHOR | SURAJ R MIRAJKAR, KEERTHANA.H.S PG Student, Dept. of MCA, City Engineering College, Bengaluru, Karnataka, India Assistant Professor, Dept. of MCA, City Engineering College, Bengaluru, Karnataka, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405054 |
| pdf/54_AI Based Optical Lenses Defect Detection System.pdf | |
| KEYWORDS | |
| References | 1. Goodfellow, I., Bengio, Y., Courville, A., “Deep Learning,” MIT Press, 2016. 2. Szeliski, R., “Computer Vision: Algorithms and Applications,” Springer, 2010. 3. LeCun, Y., Bengio, Y., Hinton, G., “Deep Learning,” Nature, 2015. 4. Zhang, Z., “Machine Vision for Industrial Inspection,” IEEE Transactions, 2020. 5. Krizhevsky, A., Sutskever, I., Hinton, G., “ImageNet Classification with Deep CNNs,” 2012. 6. Jain, A. K., “Fundamentals of Digital Image Processing,” Prentice Hall. 7. Liu, W., et al., “Real-Time Object Detection using CNN,” IEEE, 2019. 8. Redmon, J., et al., “You Only Look Once: Unified Object Detection,” 2016. 9. OpenCV Documentation, “Image Processing Techniques,” 2022. 10. IEEE Papers on AI-Based Defect Detection Systems, 2021–2024. |
