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Garbage classification using deep learning technology
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Garbage classification has always been an important issue in environmental protection, resource recycling, and social livelihood. However, garbage classification takes a lot of time and effort. Moreover, garbage classification directly affects the health of workers. Currently, due to the development of artificial intelligence, advanced garbage classification robots are being used more and more in recycling factories. With the sufficient support of robots integrated with artificial intelligence technology, garbage will be more and more quickly processed and accurately classified. Therefore, this study presents an efficient and simple garbage classification model based on deep learning technology. This model will automatically and accurately classify garbage, thereby freeing up human labors. In this paper, the ResNet-50 model was used to develop the system. The input data includes images of garbage types to perform classification, and 3 different groups of garbage will be classified. The experimental results demonstrate the effectiveness of this model.
Title: Garbage classification using deep learning technology
Description:
Garbage classification has always been an important issue in environmental protection, resource recycling, and social livelihood.
However, garbage classification takes a lot of time and effort.
Moreover, garbage classification directly affects the health of workers.
Currently, due to the development of artificial intelligence, advanced garbage classification robots are being used more and more in recycling factories.
With the sufficient support of robots integrated with artificial intelligence technology, garbage will be more and more quickly processed and accurately classified.
Therefore, this study presents an efficient and simple garbage classification model based on deep learning technology.
This model will automatically and accurately classify garbage, thereby freeing up human labors.
In this paper, the ResNet-50 model was used to develop the system.
The input data includes images of garbage types to perform classification, and 3 different groups of garbage will be classified.
The experimental results demonstrate the effectiveness of this model.
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