Abstract
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 ResNet-34 algorithm, 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. The classification accuracy of the proposed algorithm is enhanced by 1.01%. The experimental results show that the classification accuracy is as high as 99%, the classification cycle of the system is as quick as 0.95 s.
Highlights
With the rapid development of economy and the improvement of people’s living standards, the amount of garbage is increasing rapidly
The efficiency of the garbage classification still needs to be improved. It is of great academic value and practical significance to study an effective automatic garbage classification method
In order to verify the performance of the improved ResNet algorithm and activation function, evaluation indexes need to be set
Summary
With the rapid development of economy and the improvement of people’s living standards, the amount of garbage is increasing rapidly. According to the latest report of International Lianhe Zaobao, the global garbage volume will increase by 70% by 2050, and the task of garbage classification will be even more arduous. Scholars at home and abroad have done a lot of researches on garbage classification, but most of the proposed schemes are innovations of terminal recycling method [1]–[7]. In 2019, China started to require residential garbage classification, in which case the front-end collection is highly dependent on people’s awareness. The efficiency of the garbage classification still needs to be improved. It is of great academic value and practical significance to study an effective automatic garbage classification method
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