Multilevel Characteristic Weighted Fusion Algorithm in Domestic Waste Information Classification

The study of domestic waste image classification holds significant significance for fields like environmental protection and smart city development. To improve the classification efficiency of household waste information, a multi-feature weighted fusion method for household waste image classification is proposed. In this research, deep learning technology was applied to develop a multi-level feature-weighted fusion network model for domestic garbage image classification. The study first analyzed the VGG-16 architecture and created a garbage image dataset for domestic garbage according to the current Shenzhen garbage classification standard. Based on this, a multi-level feature-weighted fusion model for garbage image classification was constructed using VGG-16 as the backbone network. Furthermore, it was combined with the backbone feature extraction network as well as the content-aware and boundary-aware feature extraction networks. The performance of the classification model was tested, and it was found that the highest classification accuracy of the classification model can reach 0.98, and the shortest classification time is only 3s. The multi-level feature-weighted fusion garbage image classification model constructed in this research not only has better classification performance, but also can provide a new processing idea for the urban garbage classification problem.

@article{Li2024,
  title     = {Multilevel Characteristic Weighted Fusion Algorithm in Domestic Waste Information Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Min Li},
  doi       = {10.14569/IJACSA.2024.0151024},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151024}
}