Abstract
Solid waste is growing quickly as a result of urbanization, which is detrimental to human health and causes environmental damage. Waste management has often been a source of contention. The effects of improper waste management however, are inevitable. Solid waste is contributing 10% to the total global greenhouse gas emissions. The process for managing waste has not changed significantly over the last ten years, which has resulted in ineffective waste management techniques. A sophisticated waste management technology is required to maintain such a huge variety of solid waste since doing it manually would be exceedingly time-consuming and complex. Separating garbage into its many components, usually done by hand selecting, is one of the crucial tasks in waste management. We are proposing an innovative waste management classification system, constructed by exploiting the residual network ResNet-50, to streamline this procedure. It is a pretrained deep learning model that is applied to extract data from pictures and classify garbage into various categories, including plastic, glass, papers, etc. The garbage picture dataset was used to test this approach, and it demonstrated high accuracy on this dataset. By using this technique, it is simple and near instant to separate garbage without or with minimal human assistance. The ResNet-50 model has been trained with 100 epochs and the model achieved up to 82.1% of accuracy and has reached to the loss of 0.7715.
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Charan, N.S., Narasimhulu, T., Bhanu Kiran, G., Sudharshan Reddy, T., Shivangini Singh, T., Sunitha, G. (2023). Solid Waste Management Using Deep Learning. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_5
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DOI: https://doi.org/10.1007/978-3-031-27524-1_5
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