IoT Based Smart Vehicular Identification using Machine Learning Techniques

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ID: 312956
2024
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Abstract
Technology is growing fast, so a secure way of life and travel are in high demand among the community. A massive growth in traffic is caused by an excessively growing world population on hi-tech modern roads. There are more and more vehicles and trucks on the road. It is demand of time to identify vehicles and compute traffic jam on roads. For the control in addition monitoring systems, the identifying of vehicles is crucial. Number plates, which are a specific pairing of numbers and letters are used to identifying vehicles. However, physically identifying every single parked or moving car's license plate is a difficult and time-consuming task for anyone. Due to the daily tremendous growth of the vehicle industry, tracking individual vehicles has become a difficult undertaking. In the majority of applications involving the movement of vehicles, the identification and detection of a vehicle Number Plate (NP) are crucial techniques. In the field of image processing, it is also a hotly debated as well as ongoing research topic. For the purpose of discovering and identifying vehicle NPs, numerous techniques, methods, and algorithms have been created. You Only Look Once (YOLO) Algorithm is applied to detect as well as classify the vehicle NP. This proposed model shows more accuracy than the previous models.
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Authors Muhammad Ahsan Jamil
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/540
Keywords Keywords not found

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