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CLC number: TP391.41

On-line Access: 2009-05-02

Received: 2008-11-11

Revision Accepted: 2008-12-16

Crosschecked: 2009-02-26

Cited: 6

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.6 P.794~799

http://doi.org/10.1631/jzus.A0820788


Application of automated image analysis to the identification and extraction of recyclable plastic bottles


Author(s):  Edgar SCAVINO, Dzuraidah Abdul WAHAB, Aini HUSSAIN, Hassan BASRI, Mohd Marzuki MUSTAFA

Affiliation(s):  Faculty of Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia

Corresponding email(s):   scavino@vlsi.eng.ukm.my

Key Words:  Computer vision, Pattern recognition, Automated sorting, Recycling


Edgar SCAVINO, Dzuraidah Abdul WAHAB, Aini HUSSAIN, Hassan BASRI, Mohd Marzuki MUSTAFA. Application of automated image analysis to the identification and extraction of recyclable plastic bottles[J]. Journal of Zhejiang University Science A, 2009, 10(6): 794~799.

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author="Edgar SCAVINO, Dzuraidah Abdul WAHAB, Aini HUSSAIN, Hassan BASRI, Mohd Marzuki MUSTAFA",
journal="Journal of Zhejiang University Science A",
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pages="794~799",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820788"
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%T Application of automated image analysis to the identification and extraction of recyclable plastic bottles
%A Edgar SCAVINO
%A Dzuraidah Abdul WAHAB
%A Aini HUSSAIN
%A Hassan BASRI
%A Mohd Marzuki MUSTAFA
%J Journal of Zhejiang University SCIENCE A
%V 10
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%@ 1673-565X
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820788

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T1 - Application of automated image analysis to the identification and extraction of recyclable plastic bottles
A1 - Edgar SCAVINO
A1 - Dzuraidah Abdul WAHAB
A1 - Aini HUSSAIN
A1 - Hassan BASRI
A1 - Mohd Marzuki MUSTAFA
J0 - Journal of Zhejiang University Science A
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SP - 794
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820788


Abstract: 
An experimental machine vision apparatus was used to identify and extract recyclable plastic bottles out of a conveyor belt. Color images were taken with a commercially available Webcam, and the recognition was performed by our homemade software, based on the shape and dimensions of object images. The software was able to manage multiple bottles in a single image and was additionally extended to cases involving touching bottles. The identification was fulfilled by comparing the set of measured features with an existing database and meanwhile integrating various recognition techniques such as minimum distance in the feature space, self-organized maps, and neural networks. The recognition system was tested on a set of 50 different bottles and provided so far an accuracy of about 97% on bottle identification. The extraction of the bottles was performed by means of a pneumatic arm, which was activated according to the plastic type; polyethylene-terephthalate (PET) bottles were left on the conveyor belt, while non-PET bottles were extracted. The software was designed to provide the best compromise between reliability and speed for real-time applications in view of the commercialization of the system at existing recycling plants.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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