Transformer and VOTE Based AI Model for Waste Classification: An Application of Recycling in Smart Cities

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As industrialization and smart city development activities increase, waste collection, classification, and planning have become important. The recycling process of waste is based on recovering its properties as they are in their natural locations, reducing pollution, and helping to create a sustainable environment. This study aims to improve the performance of deep learning models for converting organic waste into recyclable waste. In the study, a transformer-based hybrid model was proposed for waste classification. The proposed model used DinoV2, ConvNeXtV2, and ViT-B16 as the base models. Feature maps derived using these models were concatenated and then classified into different classifiers. In the last step of our model, the features obtained from different classifiers were subjected to the VOTE hard-voting method. The proposed model achieved 96.64% success in solid waste classification. The VOTE process improved the proposed model's performance from 95.58% to 96.64%. When the features obtained from the transformer-based models used in the study were classified by the classifiers, the highest accuracy, 95.37%, was achieved with the DinoV2 model. The values obtained in the proposed model are important for the future of smart and sustainable cities.