Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions.

ABSTRACT: As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy. However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. To this end, a new garbage image classification model is proposed, with the ResNet-50 network as the core architecture. Specifically, first, a redundancy-weighted feature fusion module is proposed,

SUBMITTER: Li L 

PROVIDER: S-EPMC11771864 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature