A Hybrid Deep Learning Framework with Complementary Feature Fusion for Automated Multi-Class Waste Sorting
Keywords:
Complementary feature fusion, ConvNeXt-Tiny, Ensemble learning, Hybrid CNN– Transformer, Multi-class waste classification, Swin-TinyAbstract
Sorting of the waste using automated methods of control is also an essential part of the contemporary system of waste management since it allows sorting of the material correctly and enables sustainable recycling. The paper suggests a novel hybrid deep learning framework known as multi-class waste classification that will combine the features and capabilities of Swin-Tiny Transformer and ConvNeXt-Tiny networks in the form of complementary feature learning. The proposed framework will combine both network deep feature representations at the feature level to create a single feature, as well as classify it, where the overall accuracy is 97.85%. The model achieves macro-averaged precision, recall, and F1-score of 97.37%, 97.09%, and 97.21%, respectively, and the weighted precision, recall, and F1-score of 97.87%, 97.85%, 97.85%, respectively, which shows no drift in the model and balanced performance on all the classes. The evaluation of the framework is based on a publicly accessible waste dataset collection of 15,515 images of 12 types of classes, which guarantees assessment of multiple classes. The efficacy and resilience of the suggested hybrid model have been confirmed through the use of experimental results, which show that the hybrid model is always more effective and stronger in comparison to the individual architectures.
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