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

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.