Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

Abstract

Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.

UHD Database Focus on Smart Cities and Smart Transport

“…Changing the cropping method or changing the frame width was described. The classification of municipal waste using an automated system was proposed in paper [35]. The suggested model classified the waste into multiple categories using convolutional neural networks.…”

Neurotechnologies Applied to Society's Perception of Cyber-Physical Systems (CPS) in Smart Cities

“…Experts in smart cities play a crucial role in evaluating and formulating an efficient waste management plan that can be easily integrated with the overall development plan of the entire city. Machine learning and deep learning are sought-after areas in computer science and are finding extensive applications in the development of smart cities (Malik et al, 2023). Smart cities are an example of intelligent environments where all devices are connected, and computing is done in the cloud.…”

“…Urban resource management: lighting, waste management and water supply Belsare et al, 2023;Mishra et al, 2022;Malik et al, 2023;Gupta et al, 2022…”

Web Semantic-Based MOOP Algorithm for Facilitating Allocation Problems in the Supply Chain Domain

“…Because of semantic-based methodologies, resource allocation went from a challenging optimisation problem to becoming a more informed and effective decision-making process (Akram et al, 2022), (Malik et al, 2023). The organisation profited from higher client satisfaction due to quicker shipment of orders, improved warehouse management, and decreased operational expenses (Gaurav et al, 2023).…”