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Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier.

Md Shafiqul Islam1, M A Hannan1, Hassan Basri2

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

Waste Management (New York, N.Y.)
|November 19, 2013
PubMed
Summary
This summary is machine-generated.

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This study presents an efficient image processing solution for solid waste management, achieving 98.50% accuracy in estimating waste levels. The system optimizes waste collection routing by accurately assessing bin fullness.

Area of Science:

  • Computer Science
  • Environmental Engineering
  • Artificial Intelligence

Background:

  • Solid Waste Management (SWM) faces challenges for municipalities.
  • Existing SWM systems (GIS, RFID, sensors) have limitations.
  • Accurate estimation of solid waste (SW) levels remains an unmet need.

Purpose of the Study:

  • To develop an efficient image processing solution for estimating SW levels in bins.
  • To overcome challenges in capturing centralized bin images for analysis.
  • To optimize waste collection routing through accurate fill-level estimation.

Main Methods:

  • Utilized Dynamic Time Warping (DTW) for bin area detection and cropping.
  • Employed Gabor wavelet (GW) for feature extraction from bin images.
Keywords:
Bin level classificationDTWGabor waveletMLPSolid waste bin detection

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  • Trained a Multi-Layer Perceptron (MLP) classifier to estimate waste levels.
  • Evaluated classifier performance using Receiver Operating Characteristic (ROC) curves.

    • Main Results:

    • Achieved a high accuracy of 98.50% in waste level estimation.
    • The developed system demonstrates comparable performance to existing image processing methods.
    • DTW and GW combined with MLP classifier yielded promising results.

    Conclusions:

    • The proposed image processing system effectively estimates solid waste levels.
    • This technology can significantly optimize waste collection logistics and routing.
    • Accurate waste level data enables more efficient municipal solid waste management.