Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

Maher Arebey, Hannan M A, Rawshan Ara Begum, Hassan Basri

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

This paper presents solid waste bin level detection and classification using gray level co-occurrence matrix (GLCM) feature extraction methods. GLCM parameters, such as displacement, d, quantization, G, and the number of textural features, are investigated to determine the best parameter values of the bin images. The parameter values and number of texture features are used to form the GLCM database. The most appropriate features collected from the GLCM are then used as inputs to the multi-layer perceptron (MLP) and the K-nearest neighbor (KNN) classifiers for bin image classification and grading. The classification and grading performance for DB1, DB2 and DB3 features were selected with both MLP and KNN classifiers. The results demonstrated that the KNN classifier, at KNN = 3, d = 1 and maximum G values, performs better than using the MLP classifier with the same database. Based on the results, this method has the potential to be used in solid waste bin level classification and grading to provide a robust solution for solid waste bin level detection, monitoring and management.

Original languageEnglish
Pages (from-to)9-18
Number of pages10
JournalJournal of Environmental Management
Volume104
DOIs
Publication statusPublished - 15 Aug 2012

Fingerprint

Bins
Solid wastes
solid waste
Feature extraction
Classifiers
Multilayer neural networks
matrix
image classification
extraction method
Image classification
texture
Textures
detection
waste bin
monitoring
Monitoring
parameter

Keywords

  • Classification and grading
  • GLCM
  • KNN
  • MLP
  • Solid waste monitoring and management

ASJC Scopus subject areas

  • Environmental Engineering
  • Waste Management and Disposal
  • Management, Monitoring, Policy and Law

Cite this

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach. / Arebey, Maher; M A, Hannan; Begum, Rawshan Ara; Basri, Hassan.

In: Journal of Environmental Management, Vol. 104, 15.08.2012, p. 9-18.

Research output: Contribution to journalArticle

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