Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141028(2020)

Vision-Based Automatic Detection Method for Suspended Matter in Bottled Mineral Water

Ziye Sheng** and Yunwei Zhang*
Author Affiliations
  • College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
  • show less

    Bottled mineral water must be tested to determine the presence of suspended particles prior to being dispatched from the factory. At present, manual methods are used toward this end. These methods are time-consuming and laborious, relying on artificial subjective feelings, and their detection results are not satisfactory. Aiming at this problem, an automatic detection method for suspended particles in bottled mineral water based on computer-vision technology is presented in this paper, which includes image acquisition, recognition of suspended particles, quantity statistics, size parameter detection, and other image analysis processing. On this basis, we develop an automatic detection device for suspended matter in bottled mineral water, describing the structure and working principle of the device, and accomplish the inspection test of suspended matter in bottled mineral water. Results illustrate that the proposed method can both qualitatively and quantitatively detect the number and size of suspended particles in bottled mineral water. The quantitative statistics of suspended particles is accurate. The maximum testing error for the size of suspended particles is 0.28 mm, and the relative errors are less than 6.8%. The proposed device and method could be applied to the detection of bottled mineral water prior to its dispatch from the factory with the characteristic features of accurate detection, saving labor, improving work efficiency, and easy operation.

    Tools

    Get Citation

    Copy Citation Text

    Ziye Sheng, Yunwei Zhang. Vision-Based Automatic Detection Method for Suspended Matter in Bottled Mineral Water[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141028

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Nov. 2, 2019

    Accepted: Dec. 31, 2019

    Published Online: Jul. 28, 2020

    The Author Email: Sheng Ziye (1538051646@qq.com), Zhang Yunwei (1657824262@qq.com)

    DOI:10.3788/LOP57.141028

    Topics