Design of Intelligent Manufacturing Product Identification and Detection System Based on Machine Vision

  • Shandong ZhengEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)


With the advent of the Industry 4.0 era, the development of smart factories and smart manufacturing industries has become the consensus of all countries. The intelligent manufacturing process is very complicated and usually consists of multiple links, each of which is completed by one or more intelligent manufacturing equipment. The environment perception and intelligent control technology of intelligent manufacturing equipment are the fundamental guarantee of adaptability, high precision and intelligent operation. It is also a technical problem that must be solved first in the development of intelligent manufacturing equipment. Based on the above background, the purpose of this article is the design of an intelligent manufacturing product recognition and detection system based on machine vision. In the context of the transition to intelligent manufacturing production mode, this paper proposes a method to directly generate matching templates using 3D models of cloud products. Second, this paper studies and compares various preprocessing algorithms such as image filtering and edge detection to determine bilateral filtering and Canny Edge detection performs image preprocessing, and then extracts contours from the processed binary image. Finally, based on the aforementioned theoretical research, a visual detection platform is built, and several products produced are matched with cloud storage data through experiments. The algorithm in this paper can meet the classification requirements of small and medium batches and customized products in flexible production lines, and realize the matching of actual product and cloud product model data.


Machine vision Intelligent manufacturing Image processing Product identification 


  1. 1.
    Ghosal, S., Blystone, D., Singh, A.K.: An explainable deep machine vision framework for plant stress phenotyping. Proc. Natl. Acad. Sci. 115(18), 4613–4618 (2018)CrossRefGoogle Scholar
  2. 2.
    Wang, Q., Chen, B., Zhu, D.: Machine vision-based selection machine of corn seed used for directional seeding. Nongye Jixie Xuebao/Trans. Chin. Soc. Agric. Mach. 48(2), 27–37 (2017)Google Scholar
  3. 3.
    Wang, F., Zhang, S., Tan, Z.: Non-destructive crack detection of preserved eggs using a machine vision and multivariate analysis. Wuhan Univ. J. Nat. Sci. 22(3), 257–262 (2017)CrossRefGoogle Scholar
  4. 4.
    Min, Y., Xiao, B., Dang, J.: Real time detection system for rail surface defects based on machine vision. EURASIP J. Image Video Process. 2018(1), 3 (2018)CrossRefGoogle Scholar
  5. 5.
    Chaudhury, A., Ward, C., Talasaz, A.: Machine vision system for 3D plant phenotyping. IEEE/ACM Trans. Comput. Biol. Bioinform. 16(6), 2009–2022 (2018)CrossRefGoogle Scholar
  6. 6.
    Jie, S., Yinya, L., Guoqing, Q.: Machine vision based passive tracking algorithm with intermittent observations. J. Huazhong Univ. Sci. Technol. 45(6), 33–37 (2017)Google Scholar
  7. 7.
    Xi, Q., Rauschenbach, T., Daoliang, L.: Review of underwater machine vision technology and its applications. Marine Technol. Soc. J. 51(1), 75–97 (2017)CrossRefGoogle Scholar
  8. 8.
    Shan, Z., Zhang, F., Ren, Y.: On line detection technology of the hardness of cast iron parts based on machine vision. J. Mech. Eng. 53(1), 157 (2017)CrossRefGoogle Scholar
  9. 9.
    Zhao, S., Sun, L., Li, G.: A CCD based machine vision system for real-time text detection. Front. Optoelectron. 2019(7), 1–7 (2019)Google Scholar
  10. 10.
    Zhang, H., Li, X., Zhong, H.: Automated machine vision system for liquid particle inspection of pharmaceutical injection. IEEE Trans. Instrum. Meas. 67(6), 1278–1297 (2018)CrossRefGoogle Scholar
  11. 11.
    Patel, A.K., Chatterjee, S., Gorai, A.K.: Effect on the performance of a support vector machine based machine vision system with dry and wet ore sample images in classification and grade prediction. Pattern Recogn. Image Anal. 29(2), 309–324 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Huizhou Economics and Polytechnic CollegeHuizhouChina

Personalised recommendations