A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management

The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management syste...

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Bibliographic Details
Published in:Electronics (Basel) 2021-01, Vol.10 (1), p.14
Main Authors: Kumar, Saurav, Yadav, Drishti, Gupta, Himanshu, Verma, Om Prakash, Ansari, Irshad Ahmad, Ahn, Chang Wook
Format: Article
Language:English
Subjects:
Algorithms
Biodegradability
Cardboard
Classification
Datasets
Deep learning
Ecological effects
Efficiency
Machine learning
Neural networks
Waste disposal
Waste management
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Summary:The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10010014