Classification of different types of plastics using Deep Transfer Learning

Anthony Ashwin Peter Chazhoor, Manli Zhu, Edmond S. L. Ho, Bin Gao, Wai Lok Woo*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS
EditorsPeter Galambos , Erdal Kayacan
Place of PublicationSetúbal, Portugal
PublisherSciTePress, Science and Technology Publications
Pages190-195
Number of pages6
Volume1
ISBN (Electronic)9789897585371
ISBN (Print)9781713840077
DOIs
Publication statusPublished - 2021
Event2nd International Conference on Robotics, Computer Vision and Intelligent Systems -
Duration: 27 Oct 202128 Oct 2021
https://robovis.scitevents.org/

Conference

Conference2nd International Conference on Robotics, Computer Vision and Intelligent Systems
Abbreviated titleROBOVIS 2021
Period27/10/2128/10/21
Internet address

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