Real-time vehicle litter tracking using deep learning: A case study of noTrash.ai 2.0
Authors
Shenoy, Anusha
Issue Date
2024
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights holder
Rights
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Abstract
A city longing to get tagged as smart city should always aim at the health and public safety of the citizens. Every initiative carried out by the city needs to confine itself with a boundary of safety. The boundary of safety can be defined with the aid of the artificial intelligence. In a broad view, clean and hygienic environment can be achieved by tagging the requirements with Machine Learning in every single application criterion. Cleanliness of city streets has an important impact on city environment and public health. Conventional street cleaning methods involve street sweepers going to many spots and manually confirming if the street needs to clean. However, this method takes a substantial amount of manual operations for detection and assessment of street's cleanliness which leads to a high cost for cities. To overcome this vulnerability, Real-Time Vehicle Litter Tracking Using Deep Learning: A Case Study of noTrash.ai 2.0 uses Machine Learning to stop further littering by monitoring the litters thrown from the vehicle and employs cost benefit analysis to showcase the possible profitability of implementing the proposed methodology.