Research on Defect Detection of The Liquid Bag of Bag Infusion Sets Based on Machine Vision

Authors

  • Qian Zhang
  • Kang Liu
  • Bo Huang

DOI:

https://doi.org/10.54097/ajst.v5i3.8011

Keywords:

Machine vision, Liquid bag, Light experiment, Gamma correction.

Abstract

Intravenous infusion often uses bag infusion devices for clinic treatment, and the liquid bag assembly is an essential component of the bag infusion device. This paper adopts a machine vision system to inspect the assembly quality of the pipeline and dosing interface of liquid bag assembly. We conduct in-depth research on the lighting method, image pre-processing, and defect detection algorithm of a vision system for two defects of pipeline missing and dosing interface missing. Moreover, we do a lighting experiment to detect transparent liquid bag assembly defects. Proposes an adaptive gamma correction library based on power function derivative calculation, and combines the cumulative histogram for adaptive gamma correction, which amplifies the image edge information, combined with the binarization of the OTSU, solves the image segmentation problem in this. Proposes an adaptive ROI region selection method and virtual linear scanning method to achieve the detection of two kinds of defects. The results show that the recognition rate of missing defects of the pipeline on the liquid bag assembly in the machine vision system reaches 100%.

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Published

05-05-2023

How to Cite

Zhang, Q., Liu, K., & Huang, B. (2023). Research on Defect Detection of The Liquid Bag of Bag Infusion Sets Based on Machine Vision. Academic Journal of Science and Technology, 5(3), 186–197. https://doi.org/10.54097/ajst.v5i3.8011

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Section

Articles