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Identification and Classification of Industrial Plastic Waste Using Deep Learning Models

Shetty, Tarunveer Subhash (2022) Identification and Classification of Industrial Plastic Waste Using Deep Learning Models. Masters thesis, Dublin, National College of Ireland.

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Waste management is a critical issue that all major urbanized communities must address. In today’s world, sustainable growth is targeted by major economies, and waste management is one of the areas that will help with that. Abundant research has been conducted into automating waste separation to segregate the waste from recyclable to non-recyclable. The margin for human error is reduced by the introduction of automation. Machine learning and advanced deep learning models, assist in reaching this goal by allowing for the construction of high-accuracy models. The segregation of plastic trash into plastic types was the emphasis of this study paper. The current research proposes the development of an automated system that uses deep learning models to classify plastic garbage. This study aims to develop a model that is highly accurate, scalable, and simple. The study experimented with three models, Inception-ResNet-v2, VGG19, and Xception. VGG19 delivered promising results and is better than the state-of-the-art canny edge detection-based model by almost 22%.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Plastic Waste Management; Deep Learning; Transfer learning; VGG19; Xception; Inception
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > TD Environmental technology. Sanitary engineering
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 11 Mar 2023 11:55
Last Modified: 11 Mar 2023 11:55

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