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

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

Copyright © 2023 Elsevier Ltd. All rights reserved.

Bibliographische Detailangaben
Veröffentlicht in:Waste management (New York, N.Y.). - 1999. - 174(2024) vom: 15. Feb., Seite 439-450
1. Verfasser: Hossen, Md Mosarrof (VerfasserIn)
Weitere Verfasser: Ashraf, Azad, Hasan, Mazhar, Majid, Molla E, Nashbat, Mohammad, Kashem, Saad Bin Abul, Kunju, Ali K Ansaruddin, Khandakar, Amith, Mahmud, Sakib, Chowdhury, Muhammad E H
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:Waste management (New York, N.Y.)
Schlagworte:Journal Article Convolutional Neural Network (CNN) Deep Learning Multi-label Classification Recycling Waste Classification Waste Management
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520 |a 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 
650 4 |a Journal Article 
650 4 |a Convolutional Neural Network (CNN) 
650 4 |a Deep Learning 
650 4 |a Multi-label Classification 
650 4 |a Recycling 
650 4 |a Waste Classification 
650 4 |a Waste Management 
700 1 |a Ashraf, Azad  |e verfasserin  |4 aut 
700 1 |a Hasan, Mazhar  |e verfasserin  |4 aut 
700 1 |a Majid, Molla E  |e verfasserin  |4 aut 
700 1 |a Nashbat, Mohammad  |e verfasserin  |4 aut 
700 1 |a Kashem, Saad Bin Abul  |e verfasserin  |4 aut 
700 1 |a Kunju, Ali K Ansaruddin  |e verfasserin  |4 aut 
700 1 |a Khandakar, Amith  |e verfasserin  |4 aut 
700 1 |a Mahmud, Sakib  |e verfasserin  |4 aut 
700 1 |a Chowdhury, Muhammad E H  |e verfasserin  |4 aut 
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773 1 8 |g volume:174  |g year:2024  |g day:15  |g month:02  |g pages:439-450 
856 4 0 |u http://dx.doi.org/10.1016/j.wasman.2023.12.014  |3 Volltext 
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