Skip to main content
SpringerLink
Log in

An automatic aperture detection system for LED cup based on machine vision

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In order to improve the effectiveness of aperture detection and enhance the competitiveness of enterprise, an automatic inspection system for detecting the aperture of Led cup is developed in the paper. The proposed system can achieve detecting the aperture and separating the unqualified Led cups robustly. Specifically, efficient approaches based on three-point circle fitting and convolutional neural network (CNN) are proposed to achieve automatic aperture detection. Then, a novel control unit is designed to separate the unqualified Led cups using the gas claw and air cylinder. Experimental results demonstrate that the detecting accuracy of the developed system can well meet the requirements of manufacturing enterprise. Moreover, the proposed system can greatly save time and labor costs for enterprises. In addition, with this system we can efficiently construct a vision big data of LED cups. Using such a vision big data, problems of the production line can be timely discovered, and the production quality will be greatly improved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Chen X, Zhou W, Liu H et al (2012) In-situ particle measurement with blurred image processing using telecentric lenses. IEEE International Conference on Imaging Systems and Techniques (IST) 350-355

  2. Cui J, Liu Y, Xu Y et al (2013) Tracking generic human motion via fusion of low and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002

    Article  Google Scholar 

  3. Darwish S (2012) Soft computing applied to the build of textile defects inspection system. IET Comput Vis 7(5):373–381

    Article  Google Scholar 

  4. Fan C, Liu C (2015) A novel algorithm for circle curve fitting based on the least square method by the points of the Newton's rings. 2015 International Conference on Computers, Communications, and Systems (ICCCS) 256-260

  5. Feng W, He Y, Shi FG (2011) Investigation of LED light output performance characteristics under different alternating current regulation modes. IEEE J Sel Top Quantum Electron 17(3):720–723

    Article  Google Scholar 

  6. Feng H, Jiang Z, Xie F et al (2014) Automatic fastener classification and defect detection in vision-based railway inspection systems. IEEE Trans Instrum Meas 63(4):878–888

    Article  Google Scholar 

  7. Hanzaeia SH, Afshara A (2017) Automatic detection and classification of the ceramic tiles’ surface defects. Pattern Recogn 66:174–189

    Article  Google Scholar 

  8. He K, Zhang X, Ren S et al (2016) Dep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition 770-778

  9. Inan MN, Arik M (2014) Development of figure of merits for energy efficient LED lighting system. IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 260-266

  10. Jian C, Gao J, Ao Y (2017) Automatic surface defect detection for mobile phone screen glassbased on machine vision. Appl Soft Comput 52:348–358

    Article  Google Scholar 

  11. Jiang L (2012) Efficient randomized Hough transform for circle detection using novel probability sampling and feature points. Optik 123:1834–1840

    Article  Google Scholar 

  12. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Commun ACM 60(2):2012

    Google Scholar 

  13. Leel J, Park P (2013) Inspection of defect on lcd panel using local meanalgorithm based on similarity. International Conference on Control, Automationand Systems 584–588

  14. Leo GD, Liguori C, Pietrosanto A et al (2017) A vision system for the online quality monitoring of industrial manufacturing. Opt Lasers Eng 89:162–168

    Article  Google Scholar 

  15. Li Q, Ren S (2012) A real-time visual inspection system for discrete surface defects of rail heads. IEEE Trans Instrum Meas 61(8):2189–2199

    Article  Google Scholar 

  16. Li X, Zhang J (2016) A joint method for the maximum inscribed circle and minimum circumscribed circle. Measurement 87:189–193

    Article  Google Scholar 

  17. Liao H, Chen Z, Zhang X (2014) Calibration of camera with small FOV and DOF telecentric lens. IEEE International Conference on Robotics and Biomimetics (ROBIO) 498-503

  18. Lisanti G, Karaman S, Pezzatini D et al (2015) A multi-camera image processing and visualization system for train safety assessment. Multimed Tool Appl 13(2):1–22

    Google Scholar 

  19. Liu Y, Nie L, Liu L et al (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  20. Liu L, Cheng L, Liu Y et al (2016) Recognizing complex activities by a probabilistic interval-based model. Thirtieth AAAI Conference on Artificial Intelligence 1266-1272

  21. Liu F, Xu G, Liang L et al (2016) Minimum circumscribed circle and maximum inscribed circle of roundness deviation evaluation with intersecting chord method. IEEE Trans Instrum Meas 65(12):2787–2796

    Article  Google Scholar 

  22. Lu Y, Wei Y, Liu L et al (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tool Appl 76(8):10701–10719

    Article  Google Scholar 

  23. Marfia G, Roccetti M (2016) A practical computer based vision system for posture and movement sensing in occupational medicine. Multimed Tool Appl 76(6):8109–8129

  24. Meng F, Xu C, Li H et al (2011) Quick algorithm of maximum inscribed circle method for roundness evaluation. Intern Conf Syst Sci Eng Des Manufact inform 2:348–351

    Google Scholar 

  25. Rehman MMU, Shabbir H, Rehman SA et al (2013) A comparative analysis of electrical and photo characteristics of LED lights. 10th International Conference on Frontiers of Information Technology (FIT) 219-224

  26. Sanchis J, Guerrero D, Olivas E et al (2012) Detectingrottenness caused by penicillium genus fungi in citrus fruits usingmachine learning techniques. Expert Syst Appl 39(1):780–785

    Article  Google Scholar 

  27. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations

  28. Sun Y, Che R (2003) Novel method for solving maximum inscribed circle. Opt Precis Eng 11(2):181–185

    Google Scholar 

  29. Szegedy C, Liu W, Jia Y et al (2015) Going Deeper with Convolutions. IEEE Conference on Computer Vision and Pattern Recognition 1-9

  30. Wang Z, Hu B (2013) Algorithm of semicircular laser spot detection based on circle fitting. Infrared Laser Eng 43(6):708–711

    Google Scholar 

  31. Wang J, Liu Y, Zhang D et al (2017) A new computer vision based multi-indentation inspection system for ceramics. Multimed Tool Appl 76(2):2495–2513

  32. Yang Y, Zha ZJ, Gao M et al (2016) A robust vision inspection system for detecting surface defects of film capacitors. Signal Process 124(C):54–62

    Article  Google Scholar 

  33. Yao Z, Yi W (2016) Curvature aided Hough transform for circle detection. Expert Syst Appl 51:26–33

    Article  Google Scholar 

  34. Zidek K, Maxim V, Pitel J et al (2016) Embedded vision equipment of industrialrobot for inline detection of product errorsby clustering–classification algorithms. Int J Adv Robot Syst 13:1–10

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Zhejiang Natural Science Foundation (LY17F010020), Zhejiang science and technology project (2018C01069), and Natural Science Foundation of China (U1609216). The authors would like to thank all the anonymous reviewers for the constructive comments and useful suggests that led to improvements in the quality and organization of this paper. We also thank Jing Zhang and Yujie Du for provide valuable advices and assistance.

Author information

Authors and Affiliations

  1. School of Electronics and Information, HangZhou DianZi University, Hangzhou, China

    Yuxiang Yang, Yanting Lou, Mingyu Gao & Guojin Ma

Authors
  1. Yuxiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanting Lou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingyu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guojin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuxiang Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Lou, Y., Gao, M. et al. An automatic aperture detection system for LED cup based on machine vision. Multimed Tools Appl 77, 23227–23244 (2018). https://doi.org/10.1007/s11042-018-5639-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5639-8

Keywords

Search

Navigation