DOI QR코드

DOI QR Code

Multi-Vision-based Inspection of Mask Ear Loops Attachment in Mask Production Lines

마스크 생산 라인에서 다중 영상 기반 마스크 이어링 검사 방법

  • JiMyeong, Woo (Department of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • SangHyeon, Lee (Department of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Heoncheol, Lee (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
  • Received : 2022.10.14
  • Accepted : 2022.11.18
  • Published : 2022.12.31

Abstract

This paper addresses the problem of vision-based ear loops ansd attachment inspection in mask production lines. This paper focuses on connections with ear loops and mask filter by an efficient combined approach. The proposed method used a template matching, shape detection and summation of histogram with preprocessing. We had a parameter for detecting defects heuristically. If the shape vertices are lower than the parameters our proposed method will find defective mask automatically. After finding normal masks in mask ear loops attachment status inspection algorithm our proposed method conducts attachment amount inspection. Our experimental results showed that the precision is 1 and the recall is 0.99 in the mask attachment status inspection and attachment amount inspection.

Keywords

Acknowledgement

이 연구는 2020년 국립대학 육성사업비로 지원되었음.

References

  1. H. Lee, H. Lee, "Average Blurring-based Anomaly Detection for Vision-based Mask Inspection Systems," 2021 21st International Conference on Control, Automation and Systems (ICCAS), 2021, pp. 2144-2146, doi: 10.23919/ICCAS52745.2021.9649945.
  2. S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, J. A. Benediktsson, "Deep Learning for Hyperspectral Image Classification: An Overview," in IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 9, pp. 6690-6709, Sept. 2019, doi: 10.1109/TGRS.2019.2907932.
  3. R. Chauhan, K. K. Ghanshala, R. C. Joshi, "Convolutional Neural Network (CNN) for Image Detection and Recognition," 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 278-282, doi: 10.1109/ICSCCC.2018.8703316.
  4. S. B. Yang, S. J. Lee, "Improved CNN Algorithm for Object Detection in Large Images," Journal of the Korea Society of Computer and Information, Vol. 25, No. 1, pp. 45-53, 2020.
  5. N. Nasaruddin, K. Muchtar, A. Afdhal, A. P. J. Dwiyantoro, "Deep Anomaly Detection Through Visual Attention in Surveillance Videos," Journal of Big Data Vol. 7, No. 1, pp. 1-17, 2020. https://doi.org/10.1186/s40537-019-0278-0
  6. S. Mayannavar, U. Wali, V. M. Aparanji, "A Novel ANN Structure for Image Recognition", arXiv:2010.04586, 2020.
  7. K. Madani, "Artificial Neural Networks Based Image Processing &Pattern Recognition: From Concepts to Real-World Applications," 2008 First Workshops on Image Processing Theory, Tools and Applications, pp. 1-9, doi: 10.1109/IPTA.2008.4743797, 2008.
  8. C. Song, F. Yang, P. Li, "Rotation Invariant Texture Measured by Local Binary Pattern for Remote Sensing Image Classification," 2010 Second International Workshop on Education Technology and Computer Science, pp. 3-6, doi: 10.1109/ETCS.2010.37, 2010.
  9. D. Huang, C. Shan, M. Ardabilian, Y. Wang, L. Chen, "Local Binary Patterns and Its Application to Facial Image Analysis: A Survey," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 41, No. 6, pp. 765-781, doi: 10.1109/TSMCC.2011.2118750, 2011.
  10. F. Guo, J. Yang, Y. Chen, B. Yao, "Research on Image Detection and Matching Based on SIFT Features," 2018 3rd International Conference on Control and Robotics Engineering (ICCRE), pp. 130-134, doi: 10.1109/ICCRE.2018.8376448, 2018.
  11. R. N. Satare, S. R. Khot, "Image Matching with SIFT Feature," 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 384-387, doi: 10.1109/ICISC.2018.8399100, 2018.
  12. M. Muthugnanambika, S. Padmavathi, "Feature Detection for Color Images Using SURF," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1-4, doi: 10.1109/ICACCS.2017.8014572, 2017.
  13. Z. Zhu, G. Zhang, H. Li, "SURF Feature Extraction Algorithm Based on Visual Saliency Improvement." International Journal of Engineering and Applied Sciences, Vol. 5, No. 3, 257267, Mar. 2018.
  14. J. Canny, "A Computational Approach to Edge Detection," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, pp. 679-698, 1986. https://doi.org/10.1109/TPAMI.1986.4767851
  15. R. J. Danos, A. R. Frey, Y. Wang, "Canny Algorithm: A New Estimator for Primordial Non-Gaussianities", arXiv:1108.2265, 2011.
  16. J. Malik, S. Belongie, T. Leung, J. Shi, "Contour and Texture Analysis for Image Segmentation", International Journal of Computer Vol. 43, No. 1, pp. 7-27, 2001.
  17. S. Liu, X. Peng, Z. Liu, "Image Quality Assessment through Contour Detection," 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), pp. 1413-1417, doi: 10.1109/ISIE.2019.8781416, 2019.
  18. P. Mukhopadhyay, B. B. Chaudhuri, "A servey of Hough Transform", Pattern Recognition, Vol. 48, No. 3, pp. 993-1010, 2015. https://doi.org/10.1016/j.patcog.2014.08.027
  19. D. Duan, M. Xie, Q. Mo, Z. Han, Y. Wan, "An Improved Hough Transform for line Detection," 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). Vol. 2. IEEE, pp. V2-354, 2010.
  20. N. S. Hashemi, R. B. Aghdam, A. S. B. Ghiasi, P. Fatemi, "Template Matching Advances and Applications in Image Analysis", arXiv:1610.07231, 2016.
  21. Y. Yu, Z. Tu, L. Lu, X. Chen, H. Zhan, Z. Sun, "An Improved Template Matching Method for Object Detection", Proceedings of the 29th ACM International Conference on Multimedia, pp. 2753, 2021.