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Waste level detection and HMM based collection scheduling of multiple bins

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Listed:
  • Fayeem Aziz
  • Hamzah Arof
  • Norrima Mokhtar
  • Noraisyah M Shah
  • Anis S M Khairuddin
  • Effariza Hanafi
  • Mohamad Sofian Abu Talip

Abstract

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.

Suggested Citation

  • Fayeem Aziz & Hamzah Arof & Norrima Mokhtar & Noraisyah M Shah & Anis S M Khairuddin & Effariza Hanafi & Mohamad Sofian Abu Talip, 2018. "Waste level detection and HMM based collection scheduling of multiple bins," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-14, August.
    • Handle: RePEc:plo:pone00:0202092
    DOI: 10.1371/journal.pone.0202092
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    Cited by:

    1. Sushruta Mishra & Lambodar Jena & Hrudaya Kumar Tripathy & Tarek Gaber, 2022. "Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-22, August.

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