Review
Version 1
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Deep Learning in Waste Management: A Brief Survey
Version 1
: Received: 6 July 2024 / Approved: 8 July 2024 / Online: 9 July 2024 (03:36:37 CEST)
How to cite: Kunwar, S.; Alade, A. S. Deep Learning in Waste Management: A Brief Survey. Preprints 2024, 2024070637. https://doi.org/10.20944/preprints202407.0637.v1 Kunwar, S.; Alade, A. S. Deep Learning in Waste Management: A Brief Survey. Preprints 2024, 2024070637. https://doi.org/10.20944/preprints202407.0637.v1
Abstract
The rapid growth of the global population is causing a significant increase in waste production, leading to serious environmental and public health challenges. To address these issues, waste management systems are incorporating advanced technologies. Machine learning and computer vision are used to predict waste patterns, optimize collection schedules, and improve sorting accuracy. Deep learning automates the sorting process, provides predictive analytics, and enhances recycling rates. Robotics, combined with AI and computer vision, improves sorting efficiency, while the Internet of Things (IoT) monitors waste levels and optimizes collection routes. Despite these benefits, challenges such as data scarcity, high computational demands, and the need for substantial infrastructure investments must be addressed. This research explores the integration of advanced technologies into waste management and evaluates their effectiveness using waste datasets. It highlights the potential to tackle environmental challenges and lay the groundwork for more intelligent waste management solutions.
Keywords
deep learning; waste management; waste classification benchmarks; waste datasets benchmarks; waste detection benchmarks
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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