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

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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.

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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.

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Authors and Affiliations

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

    Yuxiang Yang, Yanting Lou, Mingyu Gao & Guojin Ma

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  1. Yuxiang Yang
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  2. Yanting Lou
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  3. Mingyu Gao
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  4. Guojin Ma
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Correspondence to Yuxiang Yang.

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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

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