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Shirbhate, Samiksha (2024) Efficient Waste Segregation Using Deep Learning Technique. Masters thesis, Dublin, National College of Ireland.
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Abstract
Waste generation is increasing day by day and causes harm to the nature and growth of the economy as the maximum of things are directly dumped instead of recycled. On the other hand, the recycling rate is rising but so is the waste, and thus the recycling rate should be as maximum as possible. This can be achievable only if the waste is properly segregated so that this classified waste is sent to recycling. Machine learning has the potential to enable the creation of highly accurate models that help to achieve this objective. Numerous studies have been carried out to automate trash classification with minimal human involvement. Therefore, the goal of this research is to contribute to the creation of a better model. So the model proposed is a novel technique which a combination of two models YOLORYOLOv8. As a result, there are two models that have shown great performance at 70 epochs which are YOLOv8 and the combined model YOLOR-YOLOv8. Whereas YOLOR P6 is also improved over epochs.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Muntean, Christina Hava UNSPECIFIED |
| Uncontrolled Keywords: | Waste segregation; deep learning; YOLOR; YOLOv8; YOLORYOLOv8; CNN-based models; YOLOv series model; YOLOR P6; YOLOR W6; implicit knowledge; explicit knowledge |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 05 Jun 2025 13:41 |
| Last Modified: | 05 Jun 2025 13:41 |
| URI: | https://norma.ncirl.ie/id/eprint/7761 |
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