An Intelligent Fine Garbage Classification Framework for Urban Construction Management Based on Improved Deep Learning Models
For automated garbage classification in urban construction management,a deep learning framework based on improved Swin Transformer (SWT) and Convolutional Neural Network (CNN) is proposed. The SWT is utilized as the benchmark model for garbage classification,leveraging hierarchical Transformer structure to compute feature representations through shifting windows. Thus,while maintaining locality,cross-window connections are allowed to improve image processing efficiency. A Feature Strengthening (FS) module is added at the end of each SWT module to enhance feature extraction capabilities. The problem of class imbalance is addressed through the design of a weighted cross-entropy loss function. Performance results on a publicly available garbage separation dataset show that the proposed framework achieves the best performance in terms of accuracy,precision and recall,with a classification accuracy of 98.93％,surpassing current state-of-the-art methods. The proposed method enables effective automated garbage classification,improves waste processing efficiency,enhances urban image,and promotes the development of cities towards a smarter,greener and more sustainable direction.
deep learningSwin TransformerConvolutional Neural Networkgarbage classificationfeature enhancementshifting windows
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