Abstract
Segregation and recycling of waste have been recognized to be vital for both economic and ecological reasons, with industries demanding high efficiency. However, current studies on automatic waste detection lack benchmarks and widely accepted standards, making comparisons difficult. This chapter addresses the issue by employing deep learning for waste classification into two categories: recyclable and non-recyclable materials. The dataset explained in this chapter has been compiled from various sources, ensuring a diverse representation of waste types. The garbage classification model is trained on the MobileNetV2 deep neural network architecture, enabling rapid and accurate classification of domestic waste. The model achieved an impressive 94% absolute accuracy, in 15 epochs, showcasing its efficiency and effectiveness. The applications of this research aim to provide better waste categorization and encourage more widespread recycling practices. By leveraging deep learning techniques, the proposed model streamlines the waste-sorting process, potentially saving substantial labor, material, and time costs associated with manual sorting. The development of efficient and accurate waste classification systems can significantly contribute to environmental sustainability efforts and promote a circular economy. The proposed waste classification model automates plastic segregation by making the process more efficient and faster.
