Abstract
With the development of society, intelligent garbage classification was totally indispensable in life, and it was the key to intelligent garbage classification for the more superior image recognition technology. In order to solve the key problem of garbage identification technology, this paper designed a garbage image classification system based on transfer learning network model to recognize and classify a variety of common garbage images. By comparing the performance of the pretrained models of Alexnet, VGG, Res-Net and Mobile-Net, the optimal recognition accuracy reached more than 93%, and the most suitable network model for deployment and Mobile Terminal was selected, which provided technical support for intelligent garbage identification.
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Acknowledgements
This work was financed by the Technological Project of Heilongjiang Province “The open competition mechanism to select the best candidates” (No. 2022ZXJ05C01), Funding for the Opening Project of Key Laboratory of Agricultural Renewable Resource Utilization Technology (No. HLJHDNY2114), Key project of the 14th Five-Year Plan of Education Science of Heilongjiang Province in 2021(No.GJB1421563), and Heilongjiang University of Science and Technology the introduction of high-level talent research start-up fund projects (No. 000009020315).
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Zhao, Q., Xiong, C., Liu, K. (2023). Design and Implementation of Garbage Classification System Based on Convolutional Neural Network. In: Li, A., Shi, Y., Xi, L. (eds) 6GN for Future Wireless Networks. 6GN 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-36014-5_15
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DOI: https://doi.org/10.1007/978-3-031-36014-5_15
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