Automatic License Plate Recognition for Indian Roads Using Faster-RCNN

Ravirathinam, Praveen; Patawari, Arihant

doi:10.1109/icoac48765.2019.246853

Cited by 22 publications

(5 citation statements)

References 6 publications

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“…The authors in [29] detected frontal car views and LPs using a single RCNN setup in a cascade fashion, achieving good recall and precision rates. In this instance, references [30][31][32] updated RCNNs expressly for LP detection and demonstrated that the enhanced versions outperformed the originals.…”

Section: Literature Reviewmentioning

confidence: 99%

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Alharbi,

Alshahrani,

Zakariah

et al. 2023

CMC

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Privacy and trust are significant issues in intelligent transportation systems (ITS). Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels, optical fiber, and blockchain technology. The Internet of Things (IoT) is a network of connected, interconnected gadgets. Privacy issues occasionally arise due to the amount of data generated. However, they have been primarily addressed by blockchain and smart contract technology. While there are still security issues with smart contracts, primarily due to the complexity of writing the code, there are still many challenges to consider when designing blockchain designs for the IoT environment. This study uses traditional blockchain technology with the "You Only Look Once" (YOLO) object detection method to accurately locate and identify license plates. While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising, they have received limited research attention. Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network (RCNN) built with the tensor flow core open source libraries. This method works reasonably well for identifying any license plate. The Automatic License Plate Recognition (ALPR) approach delivered outstanding results in various datasets. First, with a recognition rate of 96.2%, our system (UFPR-ALPR) surpassed the previously used technology, consisting of 4500 frames and around 150 films. Second, a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset. Third, the license plate's characters were complicated for standard methods to identify because of the shifting lighting correctly. The proposed model, however, produced beneficial outcomes. KEYWORDS Intelligent transportation system; blockchain technology; license plate recognition; privacy; YOLO; deep learning technique; ALPRAdditionally, noise removal techniques such as Gaussian blur and median filtering were applied to remove any noise or artifacts in the license plate images. Finally, the pre-processed dataset was split into training, and testing sets with a ratio of 80:20 to evaluate the model's performance on unseen data. These pre-processing techniques ensure that the license plate recognition model is trained on a high-quality dataset, leading to improved accuracy in the recognition process. Materials and MethodsInformation about traffic management, environmental changes, and congestion is gathered and disseminated through a transportation system known as ITS. Communications between vehicles and between vehicles and infrastructure are made possible by ITS in various ways. In addition, ITS includes information from both wired and wireless connections. Infrastructure-to-infrastructure communication typically uses cable technology, while V2V and V2I communication frequently use wireles...

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Section: Literature Reviewmentioning

confidence: 99%

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Alharbi,

Alshahrani,

Zakariah

et al. 2023

CMC

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show abstract

“…A bounding box is created to determine the exact coordinates that surround the vehicles' license plates. The construction of this detector will be carried out using the YOLO [1] transfer learning model and Faster RCNN [2], [3]which will be trained on a dataset consisting of a diverse variety of vehicle images, like normal vehicles, taxis, army vehicles, bikes, etc.…”

Section: I) Number Plate Detectionmentioning

confidence: 99%

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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“…Over the past few years, many researchers have addressed the license plate detection task. For example, the authors of [ 13 ] used ResNet-50-based Faster-RCNN for Indian license plate detection and achieved a very high mAP. Mean Average Precision (mAP) is a commonly used measurement of precision for object detection models and measures the crossing of the predicted bounding box with the labelled bounding box.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Memory and Time-Efficient ALPR System Based on YOLOv5

Batra

Hussain

Ahad

et al. 2022

Sensors

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With the rapid development of deep learning techniques, new innovative license plate recognition systems have gained considerable attention from researchers all over the world. These systems have numerous applications, such as law enforcement, parking lot management, toll terminals, traffic regulation, etc. At present, most of these systems rely heavily on high-end computing resources. This paper proposes a novel memory and time-efficient automatic license plate recognition (ALPR) system developed using YOLOv5. This approach is ideal for IoT devices that usually have less memory and processing power. Our approach incorporates two stages, i.e., using a custom transfer learned model for license plate detection and an LSTM-based OCR engine for recognition. The dataset that we used for this research was our dataset consisting of images from the Google open images dataset and the Indian License plate dataset. Along with training YOLOv5 models, we also trained YOLOv4 models on the same dataset to illustrate the size and performance-wise comparison. Our proposed ALPR system results in a 14 megabytes model with a mean average precision of 87.2% and 4.8 ms testing time on still images using Nvidia T4 GPU. The complete system with detection and recognition on the other hand takes about 85 milliseconds.

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Automatic License Plate Recognition for Indian Roads Using Faster-RCNN

Cited by 22 publications

References 6 publications

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Automatic Number Plate Detection Using Deep Learning Techniques

A Novel Memory and Time-Efficient ALPR System Based on YOLOv5

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