Waste Segregation System using Image Classification and Deep Learning for Smart Cities - SmartWasteNet

Abstract

Nowadays, automated waste segregation systems are crucial for sustainability and urban hygiene, effective waste management has become a major concern for smart cities in the modern era. In recent years, deep learning-based methods and image classification have demonstrated potential for waste identification automation. However, traditional approaches, like the base method described in Knowledge Based Systems, have drawbacks such as flat classification pipelines, low robustness to obscured or cluttered waste images, and only Convolution Neural Network (CNN) for feature extraction. Moreover, SmartWasteNet where a hybrid deep learning framework that combines Transformer-based global context modelling along with Deep Pyramid Convolutional Neural Network (DP-CNN) for multi-scale local feature extraction and confidence-guided hierarchical classification strategy for adaptive decision-making, to address these issues. Firstly, TACO (Trash Annotation in Context) and TrashNet dataset which contain different waste images conducts conceptual preprocessing using hierarchical labelling into coarse (organic vs. non-organic) and fine (plastic, metal, glass, paper, hazardous, residual) categories. Furthermore, Discriminative representations are created by fusing global and local features, and they are initially categorized at the coarse level. Moreover, identification of waste types and low-confidence predictions are adaptively routed to a fine-grained classifier. Furthermore, the Scalability and practical application are guaranteed by software design. Additionally, the efficacy of SmartWasteNet for intelligent urban waste segregation is demonstrated by experimental results that show a significant improvement in accuracy, precision, recall, F1-score, robustness to occlusion, and adaptivity when compared to the base method. Finally, adaptive hierarchical decision making is made by this novel model. The primary drawback is the increased conceptual complexity brought due to hierarchical architecture.

Introduction

Rapid urbanization and population growth have significantly increased municipal solid waste, creating serious environmental, economic, and public health challenges for smart cities. Efficient waste management—particularly waste segregation at the source—is essential for improving recycling rates, reducing landfill use, and supporting sustainable urban development. However, manual segregation is labor-intensive, time-consuming, and prone to errors, making it unsuitable for large-scale urban environments.

Recent advances in artificial intelligence, especially image-based deep learning and Convolutional Neural Networks (CNNs), have enabled automated waste segregation systems capable of classifying waste into categories such as organic, recyclable, and hazardous. While CNNs and advanced models like ResNet, DenseNet, and ensemble frameworks have shown promising accuracy, they face limitations including high computational complexity, large data requirements, poor performance under challenging real-world conditions (e.g., occlusion, lighting variations), and difficulty deploying on low-power embedded devices used in smart bins.

To address these challenges, this research proposes SmartWasteNet, a hybrid deep learning–based waste segregation framework designed for smart city applications. The system combines a Deep Pyramid CNN (DP-CNN) for multi-scale local feature extraction with a Transformer-based model for global contextual understanding. Waste images from real-world datasets such as TACO and TrashNet are preprocessed using normalization, resizing, and data augmentation. A confidence-guided hierarchical classification strategy is then applied, first categorizing waste into organic or non-organic classes and, when confidence is low, further refining classification into detailed categories such as plastic, metal, glass, paper, residual, and hazardous waste.

Experimental results demonstrate that SmartWasteNet outperforms traditional CNN-based and hybrid models, achieving 96.1% accuracy, 95.3% precision, 95.0% recall, and 95.1% F1-score. Comparative analysis shows significant improvements over existing multi-layer CNN approaches. The discussion highlights that SmartWasteNet’s ability to integrate local and global features, along with adaptive decision-making, improves robustness, scalability, and reliability under complex real-world conditions.

Overall, SmartWasteNet provides an efficient, accurate, and scalable solution for intelligent waste segregation, supporting cleaner cities, reduced environmental impact, and sustainable smart city development.

Conclusion

This research presented SmartWasteNet which is an intelligent deep learning–based waste segregation framework fitted for smart city environments. Moreover, the proposed model integrates Deep Pyramid Convolutional Neural Networks that to capture multi-scale local visual features and Transformer-based global context modeling to learn long-range dependencies within complex waste scenarios. Further, a confidence-guided hierarchical classification strategy was performed to allow adaptive coarse-to-fine waste categorization, efficiently addressing the disadvantages of flat CNN-based pipelines, like reduced robustness under occlusion and visual ambiguity. Experimental evaluations on benchmark waste image datasets demonstrated that the proposed framework achieves consistent improvements in classification reliability and adaptability when compared with existing approaches. In fact, the hierarchical architecture introduces additional conceptual complexity; it allows decision scalability and reliability for real-world urban deployments. Moreover, future work will examine model efficiency and simplification optimization to support real-time operations and broader smart city integration.

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Copyright

Copyright © 2026 Kurella Vishnu Priya, Neeli Keerthi Satya Priya, Yerroju Sowmya, Uppuleti Bhavani, Dr. S. Srinivas, Dr. B. Venkataramana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.