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A Smart Garbage Classification based on Deep Learning
Summary
Researchers developed an AI-powered garbage classification system using deep learning to automatically sort waste categories. Accurate automated waste sorting could improve plastic recycling rates, reducing the amount of plastic that eventually breaks down into environmental microplastics.
The Garbage categorization has long been a significant problem for resource recycling, environmental protection, and social well-being. To increase the efficiency of front-end garbage collection, a deep learning-based autonomous trash categorization system is proposed. The hardware framework and mobile app for the entire garbage can system are first developed. Three additional factors help to further optimize the network structure of the proposed garbage classification algorithm: the multi-feature fusion of input images, the feature reuse of the residual unit, and the development of a new activation function. The Training algorithm serves as the foundation for the proposed garbage classification algorithm. Last but not least, artificial trash data is used to demonstrate the proposed categorization system's superiority. The classification accuracy of the suggested algorithm has increased by 99%.
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