HELMET DETECTION AND LICENSE PLATE RECOGNITION USING CNN

Abstract

Right now, we're facing a lot of issues with traffic rules in India that might use some fresh perspectives to fix. There has been a rise in the number of accidents and fatalities in India caused by the traffic offense of riding motorcycles or mopeds without a helmet. The current system mostly relies on CCTV records to track traffic offenses. In such cases, the traffic police have to zoom in on the license plate to identify the offending rider if they aren't wearing a helmet. The traffic offenses are common, and the number of persons riding motorbikes is growing daily, so this takes a lot of time and effort. Imagine a system that could detect whether a motorcyclist or moped rider isn't wearing a helmet and, if found, immediately get the license plate number. Recent studies have effectively accomplished this task using various characteristics such as CNN, R-CNN, LBP, HoG, HaaR, etc. In terms of speed, accuracy, and efficiency, however, these works have their limitations when it comes to object recognition and categorization. To try to automate the process of finding drivers who don't wear helmets and getting their license plate numbers, this study developed a system called Non-Helmet Rider detection. The core idea is based on three-level deep learning for object detection. Using YOLOv2, the first level detects a person and a motorbike or moped; the second level uses YOLOv3, and the third level uses YOLOv2. The items recognized are a helmet and a license plate. After then, Optical Character Recognition is used to obtain the license plate registration number. There are some limitations and requirements placed on all of these methods, particularly the one that extracts license plates. The efficiency of the process is crucial since video is used as an input in this task. We have developed a comprehensive system that can recognize helmets and retrieve license plate numbers using the aforementioned approaches.