Real time electronic-waste classification algorithms using the computer vision based on convolutional neural network (cnn): Enhanced environmental incentives

PK Sarswat, RS Singh, SVSH Pathapati - Resources, Conservation and …, 2024 - Elsevier
An innovative approach is needed to boost the economic value of e-waste by improving
metal recovery and facilitating the separation of plastics and valuable metal components …

Machine learning for the classification and separation of e-waste

EP Zhou - 2022 IEEE MIT Undergraduate Research …, 2022 - ieeexplore.ieee.org
The amount of global e-waste is growing at a rapid rate and is projected to increase to 74.7
Mt by 2030. However, according to a recent United Nation's study in 2019, the collection and …

A computer vision-based system for real-time component identification from waste printed circuit boards

H Sharma, H Kumar - Journal of Environmental Management, 2024 - Elsevier
With an exponential increase in consumers' need for electronic products, the world is facing
an ever-increasing economic and environmental threat of electronic waste (e-waste). To …

Comparison of different computer vision approaches for e-waste components detection to automate e-waste disassembly

AM Bassiouny, AS Farhan, SA Maged… - 2021 International …, 2021 - ieeexplore.ieee.org
Electronic Waste (E-waste) is generated in a tremendous amount due to our increasing
dependence on electronic devices and rapid upgrading in technological innovations …

[HTML][HTML] Application of deep learning object classifier to improve e-waste collection planning

P Nowakowski, T Pamuła - Waste Management, 2020 - Elsevier
This study investigates an image recognition system for the identification and classification
of waste electrical and electronic equipment from photos. Its main purpose is to facilitate …

Dynamic convolutional neural network based e‐waste management and optimized collection planning

CJ Latha, K Kalaiselvi, S Ramanarayan… - Concurrency and …, 2022 - Wiley Online Library
Electronic waste, also known as e‐waste, refers to electrical or electronic devices that are
discarded from households and workplaces. These used e‐wastes are meant to be …

Classification of organic and solid waste using deep convolutional neural networks

R Faria, F Ahmed, A Das, A Dey - 2021 IEEE 9th Region 10 …, 2021 - ieeexplore.ieee.org
The total amount of waste is increasing all around the world day-by-day especially in urban
areas. The increasing amount of unprocessed waste is very dangerous to mankind as it …

An automatic sorting system for electronic components detached from waste printed circuit boards

Y Lu, B Yang, Y Gao, Z Xu - Waste Management, 2022 - Elsevier
Recycling e-waste makes for eliminating the pollution to environment and recovering critical
materials as one of resource. Printed circuit boards (PCBs) serve as the important part in all …

Precision measurement for industry 4.0 standards towards solid waste classification through enhanced imaging sensors and deep learning model

LW Qin, M Ahmad, I Ali, R Mumtaz… - Wireless …, 2021 - Wiley Online Library
Achievement of precision measurement is highly desired in a current industrial revolution
where a significant increase in living standards increased municipal solid waste. The current …

Recyclable waste classification using computer vision and deep learning

N Ramsurrun, G Suddul, S Armoogum… - … zooming innovation in …, 2021 - ieeexplore.ieee.org
Recycling solid waste is an important step to reduce harmful impact such as sanitary and
health problems resulting from the over use of landfills. Yet, recycling requires the sorting of …