GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management.

Autor: Hossen MM; Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, Bangladesh. Electronic address: hossensakib27@gmail.com., Ashraf A; Chemical Engineering Department, University of Doha for Science and Technology, Doha, Qatar. Electronic address: Azad.ashraf@udst.edu.qa., Hasan M; Chemical Engineering Department, University of Doha for Science and Technology, Doha, Qatar. Electronic address: mazhar.hasanzia@udst.edu.qa., Majid ME; Computer Applications Department, Academic Bridge Program, Qatar Foundation, Doha, Qatar. Electronic address: mollamajid@gmail.com., Nashbat M; Chemical Engineering Department, University of Doha for Science and Technology, Doha, Qatar. Electronic address: Mohammad.nashbat@UDST.edu.qa., Kashem SBA; Department of Computing Science, AFG College with the University of Aberdeen, Doha, Qatar. Electronic address: saad.kashem@afg-aberdeen.edu.qa., Kunju AKA; Chemical Engineering Department, University of Doha for Science and Technology, Doha, Qatar. Electronic address: Aliyarukunju.kunju@udst.edu.qa., Khandakar A; Department of Electrical Engineering, Qatar University, Doha, Qatar. Electronic address: amitk@qu.edu.qa., Mahmud S; Department of Electrical Engineering, Qatar University, Doha, Qatar. Electronic address: sakib.mahmud@qu.edu.qa., Chowdhury MEH; Department of Electrical Engineering, Qatar University, Doha, Qatar. Electronic address: mchowdhury@qu.edu.qa.
Jazyk: angličtina
Zdroj: Waste management (New York, N.Y.) [Waste Manag] 2024 Feb 15; Vol. 174, pp. 439-450. Date of Electronic Publication: 2023 Dec 19.
DOI: 10.1016/j.wasman.2023.12.014
Abstrakt: The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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