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Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models

Author

Listed:
  • Meena Malik

    (Department of CSE, Sagar Institute of Science & Technology, Bhopal 462036, Madhya Pradesh, India)

  • Sachin Sharma

    (Department of CSE, Koneru Lakshmaiah Education Foundation, Vijaywada 522502, Andhra Pradesh, India)

  • Mueen Uddin

    (College of Computing and Information Technology, University of Doha for Science and Technology, Doha 24449, Qatar)

  • Chin-Ling Chen

    (School of Information Engineering, Changchun Sci-Tech University, Changchun 130600, China
    School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
    Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan)

  • Chih-Ming Wu

    (School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China)

  • Punit Soni

    (Department of CSE, Chandigarh University, Mohali 140413, Punjab, India)

  • Shikha Chaudhary

    (School of Computing and IT, Manipal University Jaipur, Jaipur 303007, Rajasthan, India)

Abstract

The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far include neural networks. In this paper, a detailed summarization was made of the existing deep learning techniques that have been proposed to classify waste. This paper proposes an architecture for the classification of litter into the categories specified in the benchmark approaches. The architecture used for classification was EfficientNet-B0. These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. This research proposes EfficientNet-B0 model tuning for images specific to particular demographic regions for efficient classification. This type of model tuning over transfer learning provides a customized model for classification, highly optimized for a particular region. It was shown that such a model had comparable accuracy to that of EfficientNet-B3, however, with a significantly smaller number of parameters required by the B3 model. Thus, the proposed technique achieved efficiency on the order of 4X in terms of FLOPS. Moreover, it resulted in improvised classifications as a result of fine-tuning over region-wise specific litter images.

Suggested Citation

  • Meena Malik & Sachin Sharma & Mueen Uddin & Chin-Ling Chen & Chih-Ming Wu & Punit Soni & Shikha Chaudhary, 2022. "Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7222-:d:837576
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    References listed on IDEAS

    as
    1. Kai Huang & Huan Lei & Zeyu Jiao & Zhenyu Zhong, 2021. "Recycling Waste Classification Using Vision Transformer on Portable Device," Sustainability, MDPI, vol. 13(21), pages 1-14, October.
    2. Dimitris Ziouzios & Dimitris Tsiktsiris & Nikolaos Baras & Minas Dasygenis, 2020. "A Distributed Architecture for Smart Recycling Using Machine Learning," Future Internet, MDPI, vol. 12(9), pages 1-13, August.
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    Cited by:

    1. Sujal Goel & Anannya Mishra & Garima Dua & Vandana Bhatia, 2024. "SEFWaM–deep learning based smart ensembled framework for waste management," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 22625-22653, September.
    2. Mesfer Al Duhayyim, 2023. "Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT," Sustainability, MDPI, vol. 15(9), pages 1-16, April.

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