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Chazhoor et al. Intell Robot 2022;2:1-19 Intelligence & Robotics
DOI: 10.20517/ir.2021.15

Research Article Open Access

Deep transfer learning benchmark for plastic waste

classification

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Anthony Ashwin Peter Chazhoor , Edmond S. L. Ho , Bin Gao , Wai Lok Woo 1
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Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
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School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610000, Sichuan, China.
Correspondence to: Prof. Wai Lok Woo, Department of Computer and Information Sciences, Northumbria University, Ellison
Place, Newcastle upon Tyne NE1 8ST, UK. E-mail: wailok.woo@northumbria.ac.uk
How to cite this article: Chazhoor AAP, Ho ESL, Gao B, Woo WL. Deep transfer learning benchmark for plastic waste
classification. Intell Robot 2022;2:1-19. https://dx.doi.org/10.20517/ir.2021.15

Received: 2 Nov 2021 First Decision: 3 Dec 2021 Revised: 24 Dec 2021 Accepted: 18 Jan 2022 Published: 28 Jan 2022

Academic Editors: Simon X. Yang, Nallappan Gunasekaran Copy Editor: Xi-Jun Chen Production Editor: Xi-Jun Chen

Abstract
Millions of people throughout the world have been harmed by plastic pollution. There are microscopic pieces of
plastic in the food we eat, the water we drink, and even the air we breathe. Every year, the average human
consumes 74,000 microplastics, which has a significant impact on their health. This pollution must be addressed
before it has a significant negative influence on the population. This research benchmarks six state-of-the-art
convolutional neural network models pre-trained on the ImageNet Dataset. The models Resnet-50, ResNeXt,
MobileNet_v2, DenseNet, SchuffleNet and AlexNet were tested and evaluated on the WaDaBa plastic dataset, to
classify plastic types based on their resin codes by integrating the power of transfer learning. The accuracy and
training time for each model has been compared in this research. Due to the imbalance in the data, the under-
sampling approach has been used. The ResNeXt model attains the highest accuracy in fourteen minutes.

Keywords: Plastic, transfer learning, recycling, waste, classification

1. INTRODUCTION
Plastic finds itself in everyday human activities. The mass production of plastic was introduced in 1907 by
Leo Baekeland, proved to be a boon to humankind . Over the years, plastic has increasingly become an
[1]
everyday necessity for humanity. The population explosion has a critical part in increasing domestic plastic
usage . Lightweight plastics have a crucial role in the transportation industry. Their usage in space
[2]

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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