Garbage classification has always been an important issue in environmental protection, resource recycling and social livelihood.
In order to improve the efficiency of front-end garbage collection, an automatic garbage classification system is proposed based on deep learning.
Firstly, the overall system of the garbage bin is designed, including the hardware structure and the mobile app.
Secondly, the proposed garbage classification algorithm is based on DenseNet-201algorithm, and its network structure is further optimized by three aspects, including the multi feature fusion of input images, the feature reuse of the residual unit, and the design of a new activation function.
Finally, the superiority of the proposed classification algorithm is verified with the constructed garbage data.
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