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...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.展开更多
The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The ...The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.展开更多
基金extend their appreciation to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-202248).
文摘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.
文摘The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.
