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

Maher Arebey1, M A Hannan, R A Begum

  • 1Dept. of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia.

Journal of Environmental Management
|April 10, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a method for solid waste bin level detection and classification using Gray Level Co-occurrence Matrix (GLCM) features. The K-nearest neighbor (KNN) classifier demonstrated superior performance for waste management.

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Area of Science:

  • Computer Science
  • Environmental Engineering
  • Image Processing

Background:

  • Effective solid waste management relies on accurate monitoring of bin fill levels.
  • Traditional methods for waste monitoring can be labor-intensive and inefficient.
  • Automated systems are needed for real-time detection and classification of waste levels.

Purpose of the Study:

  • To develop and evaluate a system for solid waste bin level detection and classification.
  • To investigate the effectiveness of Gray Level Co-occurrence Matrix (GLCM) feature extraction for this task.
  • To compare the performance of Multi-layer Perceptron (MLP) and K-nearest Neighbor (KNN) classifiers.

Main Methods:

  • Utilized Gray Level Co-occurrence Matrix (GLCM) for texture-based feature extraction from bin images.
  • Investigated various GLCM parameters (displacement 'd', quantization 'G') to optimize feature selection.
  • Employed Multi-layer Perceptron (MLP) and K-nearest Neighbor (KNN) classifiers for image classification and grading.

Main Results:

  • The K-nearest Neighbor (KNN) classifier, with specific parameter settings (KNN=3, d=1, max G), outperformed the Multi-layer Perceptron (MLP) classifier.
  • The GLCM feature database, formed by optimal parameter values, enabled effective classification.
  • The developed method showed robust performance in classifying and grading solid waste bin levels.

Conclusions:

  • The proposed GLCM feature-based approach offers a promising solution for automated solid waste bin level detection and monitoring.
  • The KNN classifier provides a reliable and efficient tool for waste management applications.
  • This technology has the potential to enhance solid waste management strategies through improved detection and grading.