TRAFFIC VIOLATION PREDICTION USING DEEP LEARNING BASED ON HELMETS WITH NUMBER PLATE RECOGNITION
DOI:
https://doi.org/10.29121/shodhkosh.v5.i1.2024.2661Keywords:
Number Plate Recognition, Object Detection, Object Recognition, Optical Character Recognition, Traffic ViolationsAbstract [English]
At the moment, two-wheelers are the most widely used kind of transportation. It is highly advised that both bike riders and soldiers wear helmets. Many academics are interested in object tracking in video surveillance, which is an important application and burgeoning field of study in image processing and machine learning. Finding objects in an image with a bounding box and different categories or shapes of the objects placed is called object detection. This study reviews tracking techniques, classifies them into several categories, and concentrates on significant and practical tracking approaches. After reviewing broad strategies under a scan of the literature on various methodologies, we analyze potential research areas. This study uses the YOLOv8 algorithm, an image processing technique, to identify motorcycle riders who do not wear helmets. Additionally, put the Optical Character Recognition method into practice to identify the license plate in a picture and retrieve user information. Next, determine the fine amount. Eventually, SMS services will be able to notify consumers in order to prevent motorbike accidents. We evaluate the framework in terms of speed and accuracy.
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Copyright (c) 2024 Dr. S G Balakrishnan, Dhinakaran P M, Dinesh A, Gokul M, Akash T
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