research-article

Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

Authors:

Phongsathorn Kittiworapanya,

Kitsuchart Pasupa,

Peter AuerAuthors Info & Claims

IAIT '21: Proceedings of the 12th International Conference on Advances in Information Technology

Article No.: 31, Pages 1 - 5

https://doi.org/10.1145/3468784.3471273

Published: 20 July 2021 Publication History

Abstract

We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.

References

[1]

Gavin C. Cawley and Nicola L.C. Talbot. 2010. On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. The Journal of Machine Learning Research 11 (Aug. 2010), 2079–2107.

[2]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA. IEEE Computer Society, 248–255. https://doi.org/10.1109/CVPR.2009.5206848

[3]

Terrance Devries and Graham W. Taylor. 2017. Improved Regularization of Convolutional Neural Networks with Cutout. CoRR abs/1708.04552(2017). arxiv:1708.04552http://arxiv.org/abs/1708.04552

[4]

Francesco Di Maria, Francesco Bianconi, Caterina Micale, Stefano Baglioni, and Moreno Marionni. 2016. Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation. Waste Management 48 (Feb. 2016), 344–352. https://doi.org/10.1016/j.wasman.2015.12.005

[5]

Gregory Dunnu, Thomas Hilber, and Uwe Schnell. 2006. Advanced Size Measurements and Aerodynamic Classification of Solid Recovered Fuel Particles. Energy Fuels 20, 4 (July 2006), 1685–1690. https://doi.org/10.1021/ef0600457

[6]

Shin Fujieda, Kohei Takayama, and Toshiya Hachisuka. 2017. Wavelet Convolutional Neural Networks for Texture Classification. arXiv abs/1707.07394 (July 2017). arxiv:1707.07394 [cs.CV] https://arxiv.org/abs/1707.07394v1

[7]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Los Alamitos, CA, USA, 770–778. https://doi.org/10.1109/CVPR.2016.90

[8]

L. Kandlbauer, K. Khodier, D. Ninevski, and R. Sarc. 2021. Sensor-based Particle Size Determination of Shredded Mixed Commercial Waste based on two-dimensional Images. Waste Management 120(2021), 784–794. https://doi.org/10.1016/j.wasman.2020.11.003

[9]

Silpa Kaza, Lisa C. Yao, Perinaz Bhada-Tata, and Frank Van Woerden. 2018. What a Waste 2.0. Washington, DC: World Bank, Washington, DC, USA. https://doi.org/10.1596/978-1-4648-1329-0

[10]

Karim Khodier, Sandra Antonia Viczek, Alexander Curtis, Alexia Aldrian, Paul O’Leary, Markus Lehner, and Renato Sarc. 2020. Sampling and analysis of coarsely shredded mixed commercial waste. Part I: procedure, particle size and sorting analysis. International Journal of Environmental Science and Technology 17, 2 (Feb. 2020), 959–972. https://doi.org/10.1007/s13762-019-02526-w

[11]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 60, 6 (May 2017), 84–90. https://doi.org/10.1145/3065386

Digital Library

[12]

Microsoft Research. 2016. Image Composite Editor. https://www.microsoft.com/en-us/research/product/computational-photography-applications/image-composite-editor [Online; accessed 16. Mar. 2021].

[13]

Lin Mar Oo and Nay Zar Aung. 2018. A simple and efficient method for automatic strawberry shape and size estimation and classification. Biosystems Engineering 170 (June 2018), 96–107. https://doi.org/10.1016/j.biosystemseng.2018.04.004

[14]

Juan Manuel Ponce, Arturo Aquino, Borja Millán, and José Manuel Andújar. 2018. Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling. Sensors 18, 9 (Sept. 2018), 2930. https://doi.org/10.3390/s18092930

[15]

Sebastian Raschka. 2018. Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. CoRR abs/1811.12808(2018). arxiv:1811.12808http://arxiv.org/abs/1811.12808

[16]

Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018 (18-23 June 2018). IEEE, Salt Lake City, UT, USA, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474

[17]

Renato Sarc. 2021. The “ReWaste4.0” Project–A Review. Processes 9, 5 (Apr 2021), 764. https://doi.org/10.3390/pr9050764

[18]

Renato Sarc, Alexander Curtis, Lisa Kandlbauer, Karim Khodier, KE Lorber, and Roland Pomberger. 2019. Digitalisation and intelligent robotics in value chain of circular economy oriented waste management – A review. Waste Management 95 (July 2019), 476–492. https://doi.org/10.1016/j.wasman.2019.06.035

[19]

Renato Sarc, KE Lorber, Roland Pomberger, Melanie Rogetzer, and Ernst-Michael Sipple. 2014. Design, quality, and quality assurance of solid recovered fuels for the substitution of fossil feedstock in the cement industry. Waste Management & Research 32, 7 (June 2014), 565–585. https://doi.org/10.1177/0734242X14536462

[20]

Renato Sarc, KE Lorber, Roland Pomberger, Melanie Rogetzer, and Ernst-Michael Sipple. 2019. Design, quality and quality assurance of solid recovered fuels for the substitution of fossil feedstock in the cement industry – Update 2019. Waste Management & Research 37, 9 (July 2019), 885–897. https://doi.org/10.1177/0734242X19862600

[21]

Renato Sarc and Karl E Lorber. 2013. Production, quality and quality assurance of Refuse Derived Fuels (RDFs). Waste Management 33, 9 (Sept. 2013), 1825–1834. https://doi.org/10.1016/j.wasman.2013.05.004

[22]

Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.1556

[23]

Ilya Sutskever, James Martens, George E. Dahl, and Geoffrey E. Hinton. 2013. On the Importance of Initialization and Momentum in Deep Learning. In Proceedings of the 30th International Conference on Machine Learning, ICML 2013 (Atlanta, GA, USA) (JMLR Workshop and Conference Proceedings, Vol. 28). JMLR.org, 1139–1147. https://doi.org/10.5555/3042817.3043064

Digital Library

[24]

Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of the Machine Learning Research, Vol. 97). PMLR, 6105–6114. http://proceedings.mlr.press/v97/tan19a.html

[25]

Michael De Villiers. 1994. The Role and Function of a Hierarchical Classification of Quadrilaterals. For the Learning of Mathematics 14, 1 (1994), 11–18. http://www.jstor.org/stable/40248098

[26]

Zelin Zhang, Jian guo Yang, X. Su, and Li hua Ding. 2013. Analysis of large particle sizes using a machine vision system. Physicochemical Problems of Mineral Processing Volume 49, issue 2 (2013). http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-410d7c2e-ccaf-4c7d-8d35-73c3f5528f96

Cited By

Peng WSadaghiani O(2023)An Analytical Review on the Utilization of Machine Learning in the Biomass Raw Materials, Their Evaluation, Storage, and TransportationArchives of Computational Methods in Engineering10.1007/s11831-023-09950-930:8(4711-4732)Online publication date: 24-Jun-2023
https://doi.org/10.1007/s11831-023-09950-9

Index Terms

Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning
1. Computing methodologies

Index terms have been assigned to the content through auto-classification.

Recommendations

Classification of Waste Materials using CNN Based on Transfer Learning
FIRE '22: Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation

Waste Management is important for humans as well as nature for healthy life and a clean environment. The major step for effective waste management is the segregation of waste according to its types. The advancement of technology such as hardware and ...
Cognitive cloud framework for waste dumping analysis using deep learning vision computing in healthy environment
Abstract
Increased population and usage of more unnatural particles create dangerous waste in society. Environmental Sustainability is ensured based on proper waste management solutions. There is a need for real-time monitoring of waste dumping in the ...
A Vision Based Approach to Localize Waste Objects and Geometric Features Exaction for Robotic Manipulation
Abstract
The quantity of waste generated is increasing daily, demanding an intelligent waste management system for effective and timely solutions. Pollution induced by garbage has been one of the major problems globally for a long time. The unawareness ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

IAIT '21: Proceedings of the 12th International Conference on Advances in Information Technology

June 2021

281 pages

ISBN:9781450390125

DOI:10.1145/3468784

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

The Austrian Research Promotion Agency
ASEAN-European Academic University Network

Conference

IAIT2021

IAIT2021: The 12th International Conference on Advances in Information Technology

June 29 - July 1, 2021

Bangkok, Thailand

Acceptance Rates

Overall Acceptance Rate 20 of 47 submissions, 43%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
57
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Peng WSadaghiani O(2023)An Analytical Review on the Utilization of Machine Learning in the Biomass Raw Materials, Their Evaluation, Storage, and TransportationArchives of Computational Methods in Engineering10.1007/s11831-023-09950-930:8(4711-4732)Online publication date: 24-Jun-2023
https://doi.org/10.1007/s11831-023-09950-9

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten