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Cited by 40Industrial Vision Systems and Defect DetectionIndustrial Automation and Control SystemsAdvanced Neural Network Applications

Vision-Based Sorting Systems for Transparent Plastic Granulate

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Peršak, Tadej, Viltužnik, Branka, Hernavs, Jernej, Klančnik, Simon·2020·Applied Sciences
DOI 10.3390/app10124269
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Abstract

Granulate material sorting is a mature and well-developed topic, due to its presence in various fields, such as the recycling, mining, and food industries. However, sorting can be improved, and artificial intelligence has been used for this purpose. This paper presents the development of an efficient sorting system for transparent polycarbonate plastic granulate, based on machine vision and air separation technology. The developed belt-type system is composed of a transparent conveyor with an integrated vision camera to detect defects in passing granulates. The vision system incorporates an industrial camera and backlight illumination. Individual particle localization and classification with the k-Nearest Neighbors algorithm were performed to determine the positions and conditions of each particle. Particles with defects are further separated pneumatically as they fall from the conveyor belt. Furthermore, an experiment was conducted whereby the combined performances of our sorting machine and classification method were evaluated. The results show that the developed system exhibits promising separation capabilities, despite numerous challenges accompanying the transparent granulate material.

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  • A computer vision-based robotic sorter can simultaneously detect and sort objects by colour and height, with sorting efficiency around 99%.

    “In the proposed system, colour and height sorting efficiency is around 99%.”

    From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [54]

    The Introduction describes a camera-based sorter that checks granule colour, and the Conclusions report 99.81% purity in the accepted material. But that figure is purity, not colour-and-height sorting efficiency; the paper does not report height sorting, and its nine-class classification accuracy was 90.52%. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Peršak, T., Viltužnik, B., Hernavs, J., & Klančnik, S.
Affiliations
  • University of Maribor (SI)
  • Plastika Skaza d.o.o., Selo 22, 3320 Velenje, Slovenia
Corresponding author
Peršak, Tadej
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2020-06-22
Volume
10
Issue
12
Pages
4269
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app10124269
OpenAlex
W3037625619
Cited by
40
References
0
Citations by year
2026: 2 · 2025: 10 · 2024: 5 · 2023: 8 · 2022: 6 · 2021: 7 · 2020: 2
FWCI
3.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
European Regional Development Fund
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Engineering › Control and Systems Engineering › Industrial Automation and Control Systems
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
transparent plastics, polycarbonate, machine vision, air separation, pneumatic separation, conveyor belt, defect detection, particle classification, k-nearest neighbors, recycling

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