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Waste image classification based on transfer learning and convolutional neural network.

Qiang Zhang1, Qifan Yang1, Xujuan Zhang2

  • 1Department of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu Province 730070, China.

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|September 11, 2021
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Summary
This summary is machine-generated.

Intelligent waste classification is crucial for sustainable development. This study introduces a DenseNet169 model using transfer learning, achieving over 82% accuracy in waste image classification.

Keywords:
Deep learningDenseNetImage recognitionRecyclable waste classificationTransfer learning

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Rapid economic development increases domestic waste, necessitating intelligent waste classification for sustainability.
  • Traditional waste classification methods suffer from low efficiency and accuracy.
  • Existing waste image datasets have limitations like uneven distribution and small sample sizes.

Purpose of the Study:

  • To improve the efficiency and accuracy of waste classification.
  • To propose an intelligent waste image classification model.
  • To address the limitations of existing waste datasets.

Main Methods:

  • Developed a DenseNet169 waste image classification model utilizing transfer learning.
  • Constructed a new, diverse waste image dataset (NWNU-TRASH) with balanced distribution and rich backgrounds.
  • Trained and tested the DenseNet169 model on the NWNU-TRASH dataset, using 70% for training and 30% for testing.

Main Results:

  • The DenseNet169 model achieved a classification accuracy exceeding 82%.
  • The proposed model demonstrated superior performance compared to other image classification algorithms.
  • The NWNU-TRASH dataset proved effective for training robust waste classification models.

Conclusions:

  • Transfer learning with DenseNet169 is effective for intelligent waste classification.
  • The NWNU-TRASH dataset provides a more realistic benchmark for waste image analysis.
  • The developed model offers a promising solution for efficient and accurate waste sorting.