Waste level detection and HMM based collection scheduling of multiple bins

In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model...

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Published inPloS one Vol. 13; no. 8; p. e0202092
Main Authors Aziz, Fayeem, Arof, Hamzah, Mokhtar, Norrima, Shah, Noraisyah M, Khairuddin, Anis S M, Hanafi, Effariza, Abu Talip, Mohamad Sofian
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 29.08.2018
Public Library of Science (PLoS)
Subjects
Bins
Computer and Information Sciences
Computers
Contamination
Engineering
Engineering and Technology
Garbage
Garbage collection
Hidden Markov models
Literature reviews
Markov Chains
Masks
Methods
Models, Theoretical
Municipal solid waste
Operations research
Physical sciences
Refuse Disposal - methods
Research and Analysis Methods
Scheduling
Sensors
Shape recognition
Solid waste disposal
Support vector machines
Vehicles
Waste Management - methods
Kuala Lumpur Malaysia
Malaysia
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Summary:In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model (HMM) is used to decide on the number of days remaining before waste is collected from the node. This decision is based on the HMM's previous state and current observations. The HMM waste collection scheduling seeks to maximize the number of days between collection visits while preventing waste contamination due to late collection. The proposed system was trained using 100 training images and then tested on 100 test images. Each test image contains three bins that might be shifted, rotated, occluded or toppled over. The upright bins could be empty, partially full or full of garbage of various shapes and sizes. The method achieves bin detection, waste level classification and collection day scheduling rates of 100%, 99.8% and 100% respectively.
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Current address: School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, Australia
Competing Interests: The authors have declared that no competing interests exist.
These authors also contributed equally to this work.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0202092