Title: Deep learning in waste management: a brief survey

Authors: Suman Kunwar; Abayomi Simeon Alade

Addresses: Faculty of Computer Science, Selinus University of Sciences and Literature, Ragusa, Italy ' Department of Physics, University of Ibadan, Oyo, Nigeria

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, optimise 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 optimises 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.

DOI: 10.1504/IJCAST.2024.143879

International Journal of Complexity in Applied Science and Technology, 2024 Vol.1 No.2, pp.125 - 141

Received: 13 Jul 2024
Accepted: 24 Jul 2024
Published online: 12 Jan 2025 *

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