Deep Learning Based Object Classification for Waste Management in Smart Cities

This paper presents a Deep Learning based implementation for object classification to be used in smart cities for waste management. The waste management is the most important problems in modern day society,which is to be tackled effectively. In the new era of Artificial Intelligence(AI),object classification has taken a well-defined advancement in its capabilities. Here we explore the possibilities of deep learning model for the Waste management. With the ever increasing urbanization and people’s fast paced life, waste management in cities has proven to be a hectic task. Traditional waste management systems are ineffective in the present state. We develop a fine-tunned ResNet152 model that can accurately identify different types of waste in cities with an accuracy of 98%.This proposed system aims to improve waste management and contribute to a sustainable future.

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Publication Details

Published
2025-04-03
DOI
https://doi.org/10.1109/ictest64710.2025.11042367
Primary Topic
Internet of Things and AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Deep Learning Based Object Classification for Waste Management in Smart Cities

Kishore Bhamidipati, K Rashida, G. Vamsi Krishna, Sidharth R Nambiar et al.
Internet of Things and AI
article

Deep Learning Based Object Classification for Waste Management in Smart Cities

Kishore Bhamidipati, K Rashida, G. Vamsi Krishna, Sidharth R Nambiar, C Shibijith
article en

Abstract

This paper presents a Deep Learning based implementation for object classification to be used in smart cities for waste management. The waste management is the most important problems in modern day society,which is to be tackled effectively. In the new era of Artificial Intelligence(AI),object classification has taken a well-defined advancement in its capabilities. Here we explore the possibilities of deep learning model for the Waste management. With the ever increasing urbanization and people’s fast paced life, waste management in cities has proven to be a hectic task. Traditional waste management systems are ineffective in the present state. We develop a fine-tunned ResNet152 model that can accurately identify different types of waste in cities with an accuracy of 98%.This proposed system aims to improve waste management and contribute to a sustainable future.

Sustainable cities and communities
Openalex Percentile: Top 79%
Internet of Things and AI
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