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Feature extraction using Hough transform for solid waste bin level detection and classification.

M A Hannan1, W A Zaila, M Arebey

  • 1Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia, hannan@eng.ukm.my.

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This summary is machine-generated.

This study introduces an automated solid waste bin monitoring system using image detection and classification. The system accurately identifies waste levels, achieving excellent results for waste classification.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Environmental Monitoring

Background:

  • Effective solid waste management is crucial for urban sustainability.
  • Manual monitoring of waste bins is labor-intensive and inefficient.
  • Developing automated systems can optimize waste collection routes and reduce costs.

Purpose of the Study:

  • To develop and evaluate an automated system for solid waste bin level detection and classification.
  • To utilize image processing and machine learning for accurate waste monitoring.
  • To assess the system's performance using established metrics.

Main Methods:

  • Hough transform techniques for feature extraction and line detection.
  • Feedforward Neural Network (FFNN) model for waste level classification.
  • Receiver Operating Characteristic (ROC) graph and Area Under Curve (AUC) for performance evaluation.

Main Results:

  • The FFNN model achieved high accuracy in classifying waste levels.
  • The system demonstrated excellent performance for WS-class (AUC: 0.9875) and good performance for WS-grade (AUC: 0.8293).
  • The developed system proves effective for solid waste bin monitoring.

Conclusions:

  • The proposed image detection and classification system offers a viable solution for automated solid waste bin monitoring.
  • The system's high accuracy and efficiency can benefit municipal authorities.
  • This technology supports smarter waste management strategies and environmental sustainability.