Abstract
Efficient waste classification is crucial for promoting recycling and achieving sustainable waste management. Real-world waste streams, however, often include mixed, deformed, and contaminated items, making manual sorting inefficient and error prone. A deep learning-based system for multi-class classification of heterogeneous waste using the RealWaste dataset is presented in this paper, which reflects actual disposal conditions such as cluttered backgrounds and overlapping materials. We fine-tune and evaluate several convolutional neural networks (CNNs), including InceptionV3, ResNet101, DenseNet, VGG, EfficientNet, and MobileNet. Among these, ResNet101 demonstrated the best performance, achieving a validation accuracy of 98.86%, loss of 0.0379, and 0.99 as F1 score. We also introduce hybrid models (e.g., ResNet101 + InceptionV3), which improved precision in complex categories such as textiles and miscellaneous trash. Furthermore, a confidence score evaluation strategy is proposed to assess model reliability, revealing high confidence (≥ 0.95) for visually distinct classes like vegetation, plastic, and food organics. Our findings establish a robust and scalable benchmark for deploying intelligent waste classification systems in real-world, sustainability-driven environments.
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Data availability
The datasets used in this study is publicly available from the below link: https://archive.ics.uci.edu/dataset/908/realwaste.
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Funding
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT: Ministry of Science and ICT) (No. RS-2025-23524664).
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Conceptualization, Y.K., P.B., S.M., A.P., W.K; Methodology, Y.K., P.B., S.M., A.P., W.K and M.F.I.; software, Y.K., P.B., S.M., W.K., and M.F.I.; Validation Y.K., M.F.I, and W.K., Formal analysis, Y.K., P.B., S.M., A.P.; Investigation, W.K., and M.F.I.; Resources, W.K., M.F.I., Data curation, Y.K., P.B., S.M., A.P.,., writing—original draft preparation, Y.K., P.B., S.M., A.P.,; writing—review and editing, ., W.K., and M.F.I.;,.; visualization, W.K., and M.F.I.;., Supervision M.F.I., and Y.K., and W.K.; Project administration, Y.K., and W.K, and M.F.I ; Funding acquisition, W.K., M.F.I.; All authors have read and agreed to the published version of the manuscript.
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Kumar, Y., Bhardwaj, P., Malhotra, S. et al. Deep residual and hybrid CNN models for confidence-aware real-world waste classification for sustainable waste management. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41001-8
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DOI: https://doi.org/10.1038/s41598-026-41001-8
