Waste classification strategy based on multi-scale feature fusion for intelligent waste recycling in office buildings

Abstract

Waste classification is an important measure to protect the environment. Existing waste classification methods mainly focus on scientific research, but lack attention to the challenges of waste classification in actual scenarios. For example, wastes with similar contours, similar textures, or contaminated appearance are difficult to be classified in actual scenarios. To address these issues, this paper proposes an innovative multi-scale feature fusion strategy (MFFS) to improve the classification accuracy of these wastes. MFFS combines local fine-grained features with global coarse-grained features to improve the feature expression ability of waste. However, how to effectively fuse these two features is a key challenge. This paper proposes a dual-scale feature fusion strategy, first fusing fine-grained features in the first dimension, then fusing coarse-grained features in the second dimension, and introducing spatial features to further enhance feature expression capabilities. In order to reduce the interference of background information, the model in this paper models global relationships based on convolutional features. The MFFS strategy achieved a classification accuracy of 95.5% on the self-built dataset and 94.1% on the public dataset TrashNet. The number of parameters of our model is reduced by 57.2% compared with the classic VGG16 and by 34.2% compared with the Vision Transformer. In addition, we designed an intelligent waste sorting device and deployed the MFFS model on the device to implement the application. Experiments show that our model has ideal accuracy and stability and can be promoted and applied.