Detection of License Plate Numbers and Identification of Non-Helmet Riders using Yolo v2 and OCR Method

Srilekha, B.; Kiran, K. V. D.; Padyala, Venkata Vara Prasad

doi:10.1109/icears53579.2022.9751989

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Another research involved YOLOv3 for plate detection and OCR for image enhancement and recognition. However, their method achieved an accuracy of nearly 90% for number plate detection and 91.5% for number plate recognition [9].…”

Section: Iiliterature Surveymentioning

confidence: 97%

Automatic Number Plate Detection Using Deep Learning Techniques

Athilakshmi,

Khatoria,

Srinivasan

2023

Recent Trends in Data Science and Its Applications

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Automatic Number Plate Recognition (ANPR) is a technique that reads a vehicle's license plate using photographs given to the Deep Learning model as its input. Existing ways to find and identify license plates on cars are severely limited by the low resolution and significant loss of edge data in plate photos. The process of automatic identification of license plates necessitates a high level of accuracy when cars are moving rapidly, and number plate abstraction is a difficult task due to number arrangement and environmental factors. The primary objective of this work is to create an Automatic Number Plate Detector that aims to recognize the text present in the number plates of cars, vans, bikes, trucks, and other vehicles that are either stationary or in motion with good accuracy. The recognition model was created by training it on a large dataset. The deep learning models Faster-RCNN and Yolo were employed to fetch the Region of Interest, followed by OCR for character extraction.

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Section: Iiliterature Surveymentioning

confidence: 97%

Automatic Number Plate Detection Using Deep Learning Techniques

Athilakshmi,

Khatoria,

Srinivasan

2023

Recent Trends in Data Science and Its Applications

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“…B. Srilekha, K. V. D. Kiran, and Venkata Vara Prasad Padyala in "Detection of License Plate Numbers and Identification of Non-Helmet Riders using Yolo v2 and OCR Method" [1] proposed a CNN model that uses Yolov2 for the detection of Non-Helmet Riders. HOG component vector is used in filtering the bikes.…”

Section: Literature Surveymentioning

confidence: 99%

Detection and Identification of Non-Helmet Riders and Their License Plate Numbers

Arshad¹,

Prasanthi²

2023

IJRASET

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Due to the ever-increasing number of vehicles a variety of traffic regulations are imposed and are addressed using various approaches. Two-wheelers are the most preferred and commonly used vehicles among the youth due to their cost. Bike riders are highly supposed to use helmets, but wearing helmets is often neglected by bike riders leading to accidents and deaths. Riding a bike without a helmet is a traffic offense. While riding a motorcycle/bike, the rider and as well as the pillion rider should wear a helmet according to the traffic rules of road safety. In the current system, traffic offenses are largely monitored by the traffic police by investigating CCTV records. The traffic police zoom into every frame of the CCTV record and tries to identify the license plates of non-helmet riders. This will take a lot of labor and time. The proposed system uses deep learning algorithms, CNN (convolutional neural networks) to analyze real-time video footage from cameras installed in key locations, such as roads and intersections, to identify individuals riding without helmets. The proposed system is expected to significantly reduce the number of non-helmet riding incidents and, therefore, decrease the number of fatalities and serious injuries resulting from motorcycle accidents.

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“…Manually implementing the helmet-wearing regulation takes a lot of time as well as is prone to mistakes. [1] Although it requires sophisticated image processing techniques, we can create an automated enforcement system with two cameras that is more accurate and efficient. Convolutional neural networks (CNNs) are the technique by which the proposed system automatically recognizes non-helmet riders and their license plates.…”

Section: Introductionmentioning

confidence: 99%

Numberplate Detection and Challan Generation for Non-Helmet Riders

2024

IRJMETS

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Over time, there has been an increase in motorbike accidents in a number of nations. The automated detection of traffic rule violators is a prerequisite for any intelligent traffic system.With a high population density in all of the major cities, motorcycles are one of the primary modes of transportation in a country like India. In India, there are more than 37 million two-wheeler riders.According to statistics, the majority of motorbike riders don't wear helmets in cities or even on highways. Wearing a helmet can reduce a rider's risk of incurring a head injury or catastrophic brain injury in the majority of motorcycle accident scenarios. For the sake of traffic safety, a system for automatically identifying helmets is therefore necessary. As a result, customized object detection models are constructed with a CNN-based algorithm (YOLO). When a helmetless rider is detected, the number plate is collected and the license registration number is identified using an optical character recognition system. The objective of this project is to design a computerized CNN-based detection device with custom-trained models and datasets for helmet authentication. Additionally, the Challan System is a web-based interface intended to assist users with various matters pertaining to managing and tracking traffic infractions. Essentially, the Challan System makes it possible for bike riders and police to have straightforward conversations using a web-based interface. This project concept demonstrates how users can complete tasks more quickly when Challan is available online. The web-based approach aims to minimize paperwork and conventional procedures in order to improve customer satisfaction.

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Detection of License Plate Numbers and Identification of Non-Helmet Riders using Yolo v2 and OCR Method

Cited by 8 publications

References 12 publications

Automatic Number Plate Detection Using Deep Learning Techniques

Automatic Number Plate Detection Using Deep Learning Techniques

Detection and Identification of Non-Helmet Riders and Their License Plate Numbers

Numberplate Detection and Challan Generation for Non-Helmet Riders

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