Waste level detection and HMM based collection scheduling of multiple bins

Aziz, Fayeem and Arof, Hamzah and Mokhtar, Norrima and Shah, Noraisyah Mohamed and Khairuddin, Anis Salwa Mohd and Hanafi, Effariza and Abu Talip, Mohamad Sofian (2018) Waste level detection and HMM based collection scheduling of multiple bins. PLoS ONE, 13 (8). e0202092. ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0202092.

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Official URL: https://doi.org/10.1371/journal.pone.0202092


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.

Item Type: Article
Funders: High Impact Research Grant Scheme (D000016-16001) under the Ministry of Higher Education, Malaysia
Uncontrolled Keywords: Markov Chains; Models, Theoretical; Refuse Disposal; Waste Management
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 18 Sep 2019 03:33
Last Modified: 18 Sep 2019 03:33
URI: http://eprints.um.edu.my/id/eprint/22409

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