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Title	Solid Waste Classification Using Transformer Model
ID_Doc	52257
Authors	Swamy K.A.; Madhavi G.M.; Madhavi K.R.; Tangudu N.; Krishnaveni C.V.; Chandra S.H.
Year	2025
Published	Lecture Notes in Networks and Systems, 1229 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-78940-3_29
Abstract	Waste classification is a critical component of modern waste management systems. The fusion of cutting-edge technology with the ever-pressing issue of waste management holds great promise for a sustainable and cleaner future. Integration of transformer models in waste management systems can transform the landscape of solid waste classification and redefine the way of managing waste streams. This research paper contributes to this evolving field by presenting a comprehensive investigation into the utility of transformer models for solid waste classification. This paper addresses the challenge of solid waste classification using advanced deep learning models and presents a transformer-based architecture specifically tailored for this task. The importance of accurate waste classification in reducing environmental impact, optimizing recycling, and complying with regulations is emphasized. In experimental comparisons against established models, including Simple CNN, ResNet101, MobileNet, and DenseNet121, the proposed transformer model showcased superior performance, achieving a remarkable accuracy of 96.1%. This performance highlights the potential of self-attention mechanisms in effectively capturing intricate waste features. While other models performed well, the Transformer model's results underscore its significance in waste classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Author Keywords	Deep Learning; Smart city; Solid waste; Vision transformers; Waste management systems

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