Classification of different types of plastics using Deep Transfer Learning

Chazhoor, Anthony Ashwin Peter, Zhu, Manli, Ho, Edmond S L., Gao, Bin and Woo, Wai Lok (2021) Classification of different types of plastics using Deep Transfer Learning. In: Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS. SciTePress, Science and Technology Publications, Setúbal, Portugal, pp. 190-195. ISBN 9781713840077, 9789897585371

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Abstract

Plastic pollution has affected millions globally. Research shows tiny plastics in the food we eat, the water we drink, and even in the air, we breathe. An average human intakes 74,000 micro-plastic every year, which sig- nificantly affects the health of living beings. This pollution must be administered before it severely impacts the world. We have substantially compared three state-of-the-art models on the WaDaBa dataset, which contains different types of plastics. These models are capable of classifying different types of plastic wastes which can be reused or recycled, thus limiting their wastage.

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