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

doi:10.5220/0010716500003061

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

Computer Information Sciences Department

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS
Editors	Peter Galambos , Erdal Kayacan
Place of Publication	Setúbal, Portugal
Publisher	Scitepress
Pages	190-195
Number of pages	6
Volume	1
ISBN (Electronic)	9789897585371
ISBN (Print)	9781713840077
DOIs	https://doi.org/10.5220/0010716500003061
Publication status	Published - 2021
Event	2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Duration: 27 Oct 2021 → 28 Oct 2021 https://robovis.scitevents.org/

Conference

Conference	2nd International Conference on Robotics, Computer Vision and Intelligent Systems
Abbreviated title	ROBOVIS 2021
Period	27/10/21 → 28/10/21
Internet address	https://robovis.scitevents.org/

Keywords

Deep Learning
Transfer Learning
Image Classification
Recycling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.5220/0010716500003061Licence: CC BY-NC-ND

ROBOVIS_2021_33_CRAccepted author manuscript, 510 KB

Cite this

Chazhoor, A. A. P., Zhu, M., Ho, E. S. L., Gao, B., & Woo, W. L. (2021). Classification of different types of plastics using Deep Transfer Learning. In P. Galambos , & E. Kayacan (Eds.), Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS (Vol. 1, pp. 190-195). Scitepress. https://doi.org/10.5220/0010716500003061

@inproceedings{c84e2659c86348f4b17abd711cdb6a4b,

title = "Classification of different types of plastics using Deep Transfer Learning",

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.",

keywords = "Deep Learning, Transfer Learning, Image Classification, Recycling",

author = "Chazhoor, {Anthony Ashwin Peter} and Manli Zhu and Ho, {Edmond S. L.} and Bin Gao and Woo, {Wai Lok}",

year = "2021",

doi = "10.5220/0010716500003061",

language = "English",

isbn = "9781713840077",

volume = "1",

pages = "190--195",

editor = "{Galambos }, Peter and Erdal Kayacan",

booktitle = "Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS",

publisher = "Scitepress",

note = "2nd International Conference on Robotics, Computer Vision and Intelligent Systems , ROBOVIS 2021 ; Conference date: 27-10-2021 Through 28-10-2021",

url = "https://robovis.scitevents.org/",

}

Chazhoor, AAP , Zhu, M, Ho, ESL, Gao, B & Woo, WL 2021, Classification of different types of plastics using Deep Transfer Learning. in P Galambos & E Kayacan (eds), Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS. vol. 1, Scitepress, Setúbal, Portugal, pp. 190-195, 2nd International Conference on Robotics, Computer Vision and Intelligent Systems , 27/10/21. https://doi.org/10.5220/0010716500003061

Classification of different types of plastics using Deep Transfer Learning. / Chazhoor, Anthony Ashwin Peter ; Zhu, Manli; Ho, Edmond S. L. et al.
Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS. ed. / Peter Galambos ; Erdal Kayacan. Vol. 1 Setúbal, Portugal: Scitepress, 2021. p. 190-195.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of different types of plastics using Deep Transfer Learning

AU - Chazhoor, Anthony Ashwin Peter

AU - Zhu, Manli

AU - Ho, Edmond S. L.

AU - Gao, Bin

AU - Woo, Wai Lok

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Transfer Learning

KW - Image Classification

KW - Recycling

U2 - 10.5220/0010716500003061

DO - 10.5220/0010716500003061

M3 - Conference contribution

SN - 9781713840077

VL - 1

SP - 190

EP - 195

BT - Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS

A2 - Galambos , Peter

A2 - Kayacan, Erdal

PB - Scitepress

CY - Setúbal, Portugal

T2 - 2nd International Conference on Robotics, Computer Vision and Intelligent Systems

Y2 - 27 October 2021 through 28 October 2021

ER -

Chazhoor AAP , Zhu M, Ho ESL, Gao B, Woo WL. Classification of different types of plastics using Deep Transfer Learning. In Galambos P, Kayacan E, editors, Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS. Vol. 1. Setúbal, Portugal: Scitepress. 2021. p. 190-195 Epub 2021 Oct 27. doi: 10.5220/0010716500003061

Classification of different types of plastics using Deep Transfer Learning

Abstract

Conference

Keywords

UN SDGs

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