Waste material classification using performance evaluation of deep learning models
Abstract: Waste classification is the issue of sorting rubbish into valuable categories for efficient waste management. Problems arise from issues such as individual ignorance or inactivity and more overt issues like pollution in the environment, lack of resources, or a malfunctioning system. Education, established behaviors, an improved infrastructure, technology, and legislative incentives to promote effective trash sorting and management are all necessary for a solution to be implemented. For solid waste management and recycling efforts to be successful, waste materials must be sorted appropriately. This study evaluates the effectiveness of several deep learning (DL) models for the challenge of waste material classification. The focus will be on finding the best DL technique for solid waste classification. This study extensively compares several DL architectures (Resnet50, GoogleNet, InceptionV3, and Xception). Images of various types of trash are amassed and cleaned up to form a dataset. Accuracy, precision, recall, and F1 score are only a few measures used to assess the performance of the many DL models trained and tested on this dataset. ResNet50 showed impressive performance in waste material classification, with 95% accuracy, 95.4% precision, 95% recall, and 94.8% in the F1 score, with only two incorrect categories in the glass class. All classes are correctly classified with an F1 score of 100% due to Inception V3’s remarkable accuracy, precision, recall, and F1 score. Xception’s classification accuracy was excellent (100%), with a few difficulties in the glass and trash categories. With a good 90.78% precision, 100% recall, and 89.81% F1 score, GoogleNet performed admirably. This study highlights the significance of using models based on DL for categorizing trash. The results open the way for enhanced trash sorting and recycling operations, contributing to an economically and ecologically friendly future.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Waste material classification using performance evaluation of deep learning models ; volume:32 ; number:1 ; year:2023 ; extent:15
Journal of intelligent systems ; 32, Heft 1 (2023) (gesamt 15)
- Creator
-
Al-Mashhadani, Israa Badr
- DOI
-
10.1515/jisys-2023-0064
- URN
-
urn:nbn:de:101:1-2023110914000782618620
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:59 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Al-Mashhadani, Israa Badr
Other Objects (12)
Deep learning performance for detection and classification of microcalcifications on mammography
Deep learning assisted XRF spectra classification
Deep learning for paranasal anomaly classification
Performance evaluation of lung sounds classification using deep learning under variable parameters
Deep phenotyping: deep learning for temporal phenotype/genotype classification
Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning
MonuNet: a high performance deep learning network for Kolkata heritage image classification
Deep learning for transesophageal echocardiography view classification
Snake species classification using deep learning techniques
Deep semantic learning for acoustic scene classification
GEMTELLIGENCE: Accelerating gemstone classification with deep learning
Hyperspectral image classification via contextual deep learning
Deep learning performance for detection and classification of microcalcifications on mammography
Deep learning assisted XRF spectra classification
Deep learning for paranasal anomaly classification
Performance evaluation of lung sounds classification using deep learning under variable parameters
Deep phenotyping: deep learning for temporal phenotype/genotype classification
Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning
MonuNet: a high performance deep learning network for Kolkata heritage image classification
Deep learning for transesophageal echocardiography view classification
Snake species classification using deep learning techniques
Deep semantic learning for acoustic scene classification
GEMTELLIGENCE: Accelerating gemstone classification with deep learning
Hyperspectral image classification via contextual deep learning
Deep learning performance for detection and classification of microcalcifications on mammography
Deep learning assisted XRF spectra classification
Deep learning for paranasal anomaly classification
Performance evaluation of lung sounds classification using deep learning under variable parameters
Deep phenotyping: deep learning for temporal phenotype/genotype classification
Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning
MonuNet: a high performance deep learning network for Kolkata heritage image classification
Deep learning for transesophageal echocardiography view classification
Snake species classification using deep learning techniques
Deep semantic learning for acoustic scene classification
GEMTELLIGENCE: Accelerating gemstone classification with deep learning