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Classification Method of Waste Shoes Based on Convolutional Neural Network
- 1 Sino-European School of Technology, Shanghai University, Shanghai, China
* Author to whom correspondence should be addressed.
Abstract
In the process of global sustainable development, waste recycling has an important strategic position, especially in the dual dimension of resource protection and environmental protection plays a key role, and becomes an important means to alleviate resource tension and ecological pressure. It is worth noting that the recycling and sorting of footwear products faces special challenges: the level of automation is relatively weak, the dependence on manual sorting is too high, and the traditional technology is difficult to accurately deal with the footwear products of multiple composite materials, resulting in limited resource conversion efficiency. In view of this technical bottleneck, the development of efficient intelligent sorting solutions has become an urgent need for the industry. This study systematically sorts out the recycling paths and existing problems of waste clothing and footwear, focuses on analyzing the technical features of deep learning architectures such as ResNet, Vision Transformer and MobileNet, and demonstrates their application efficiency in footwear sorting based on actual test data. The test data show that the target model achieves about 85% recognition accuracy in footwear sorting tasks, effectively verifying the application potential of intelligent algorithms in this field. The research results have practical guiding value for realizing intelligent sorting of footwear resources, improving the utilization rate of renewable resources and reducing environmental load, providing a feasible scheme reference for technological upgrading in related fields, and helping the construction of circular economy system.
Keywords
sustainable development,classification,waste shoes,deep learning
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Cite this article
Chen,Y. (2025). Classification Method of Waste Shoes Based on Convolutional Neural Network. Applied and Computational Engineering,146,83-93.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of SEML 2025 Symposium: Machine Learning Theory and Applications
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