An automated solid waste bin level detection system using a gray level aura matrix

M. A. Hannan, Maher Arebey, R. A. Begum, Hassan Basri

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

An advanced image processing approach integrated with communication technologies and a camera for waste bin level detection has been presented. The proposed system is developed to address environmental concerns associated with waste bins and the variety of waste being disposed in them. A gray level aura matrix (GLAM) approach is proposed to extract the bin image texture. GLAM parameters, such as neighboring systems, are investigated to determine their optimal values. To evaluate the performance of the system, the extracted image is trained and tested using multi-layer perceptions (MLPs) and K-nearest neighbor (KNN) classifiers. The results have shown that the accuracy of bin level classification reach acceptable performance levels for class and grade classification with rates of 98.98% and 90.19% using the MLP classifier and 96.91% and 89.14% using the KNN classifier, respectively. The results demonstrated that the system performance is robust and can be applied to a variety of waste and waste bin level detection under various conditions. © 2012 Elsevier Ltd.
Original languageEnglish
Pages (from-to)2229-2238
Number of pages2005
JournalWaste Management
DOIs
Publication statusPublished - 01 Dec 2012
Externally publishedYes

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