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Municipal solid waste classification and real-time detection using deep learning methods
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DOI：10.1016/j.uclim.2023.101462.png)
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Waste management has become a significant issue in most developing countries. Municipal solid waste generation has been steadily increasing over the past decade. Recycling is gaining importance because it is the only method to maintain a healthy and sustainable environment. Furthermore, recycling is not a fully autonomous operation; a significant amount of waste must be performed manually. New and innovative procedures must be implemented to deal with the increasing volume of waste products at recycling facilities. It is suggested that effective solid waste management systems have a practical approach to detecting and classifying waste mate-rials. This study presents CNN and Graph-LSTM, two deep-learning techniques that can recognize typical waste products when handled on a belt conveyor in waste collection systems. This con-volutional neural network-based solution is trained to use six object classes: cardboard, metal, glass, plastic, paper, and organic waste. The major advantage is the ability to model long-term dependencies, Improved performance, better generalization, and easy access The experimental findings show that the suggested system can achieve 97.5% accuracy in real-world situations, outperforming existing methods identified in the literature.
Keyword:
Recycling
convolutional neural networks
object detection
Municipal solid waste management
CNN
GLSTM
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