Abstract:
The current waste crisis in the world, which generates over 2.12 billion tons annually, requires highly advanced technological interventions. This thesis introduces AMWIS, a novel hierarchical transfer learning framework for real-time recyclable material recognition that is capable of deployment with edge computing.Problematic: Traditional manual waste sorting is inefficient, error-prone, and very labor-consuming. Binaryclassification and limited multi-class classification models (usually 4-6 categories) cannot reflect thediversity of real-world wastes.Proposed Solution: AMWIS bridges this gap with the deployment of complete 9-class waste classification,namely Cardboard, Food Organics, Glass, Metal, Miscellaneous Trash, Paper, Plastic, Textile Trash, Vegetation, using transfer learning on the Kaggle Waste Classification Dataset composed of 4,000 images. Key Contributions:1. Methodological Innovation: Hierarchical transfer learning combining EfficientNet-B3, MobileNetV3, and Vision Transformer architectures with adaptive ensemble fusion 2. Detailed Classification: 9-class taxonomy reflecting real composition of waste streams 3. Practical Deployment: Edge computing architecture enables real-time inference for resource-constrained devices. 4. Environmental Impact: Quantified resource recovery and circular economy benefits Results: AMWIS achieved 94.7% accuracy on the validation set with an inference time of 156ms per image, hence is ready for production at waste management facilities. Significance: This research connects existing works such as Aral et al. (2022) and Bircanoglu et al. (2019) to the needs of practical deployment. Unique contributions are made on hierarchical learning, ensemble optimization, and edge deployment architecture.
