Abstract
The rapid advancement of deep learning technology has led to the presentation of various network architectures for classification, making it easier to implement intelligent waste classification systems. However, existing waste classification models have problems such as low accuracy and slow processing. The current system does not utilize automatic classification. The proposed method uses Vision Transformer based on Multilayer Hybrid Convolution Neural Network for automatic waste classification (VT-MLH-CNN). The proposed method enhances the accuracy of waste classification and reduces the time taken for classification. Initially, it collects the data images, then the features are extracted, and next, it is processed into data normalization. The proposed model performs better by altering the number of network modules and connections. After this study determines the proper waste picture categorization variables, the best strategy is selected as the final model. The simulation results indicated that the suggested approach has a simplified network model and greater waste categorization accuracy compared to certain current efforts. Numerous tests on the TrashNet dataset demonstrate the usefulness of the recommended method, which achieves classification accuracy of up to 95.8%, which is 5.28% and 4.6% greater than those state-of-the-art techniques.
•Deep learning tech brings many network arches for easier waste classification systems implementation.•Proposed method improves waste classification accuracy and shortens time.•Simulation results: Proposed approach has a simpler network and improved waste categorization.•Paper proposes simple Vision Transformer-based ML-Hybrid ConvNet for innovative garbage classification.•Proposed VT-MLH-CNN model has the potential for integration in real-world sorting facilities.