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Enhancing Waste Management with a Deep Learning-based Automatic Garbage Classifier
Summary
This paper is not about microplastics; it presents a deep learning convolutional neural network system for automatically classifying garbage by material type to improve waste sorting efficiency and reduce the labor burden of manual waste management.
The global surge in waste production has triggered a range of pressing environmental concerns, including pollution and threats to public health. Effective waste management hinges on the pivotal process of garbage categorization. Yet, traditional manual sorting methods are fraught with issues such as time inefficiency, error susceptibility, and the need for extensive labour. A beacon of hope in addressing this challenge is the adoption of deep learning-based automatic garbage sorting. In this study, convolutional neural networks (CNN) are harnessed for the precise classification of waste materials. The proposed system encompasses pre-processing, feature extraction, and classification phases. The integration of this technology into waste management systems holds the promise of reducing human labour and enhancing the efficiency of garbage sorting processes.
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