Neural Network-Powered License Plate Recognition System Design

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.

Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

A novel license plate recognition method using HTD and VTD features

In this paper, a novel method of licence plate recognition (LPR) using the vertical traverse density (VTD) and horizontal traverse density (HTD) is presented. The neutral network algorithm using VTD and HTD features is also an innovation. In addition, a so called secondary recognition method which splits characters into different parts is developed. Experimental results show that it is a simple and fast algorithm, which meets the request of real time and nicety performances of LPR and thus has applied value in intelligence transportation system (ITS).

License Plate Recognition for Parking Control System by Mathematical Morphology

Nowadays, license plate recognition for parking systems is a critical task to provide automatic control of customers and payment. This paper introduces a new method for automatic recognition of license plates of vehicles by mathematical morphology. The proposed method can provide the license plate number of the plates in different light conditions, colors, sizes, and inclination （angles）. The algorithm can recognize the license plates of European Union vehicles quickly and correctly. The pattern learning of mathematical skeletons has high efficiency in the process. The performance of the algorithm is demonstrated well by the test in a parking control system.

Adversarial Attacks on License Plate Recognition Systems

The license plate recognition system(LPRS)has been widely adopted in daily life due to its efficiency and high accuracy.Deep neural networks are commonly used in the LPRS to improve the recognition accuracy.However,researchers have found that deep neural networks have their own security problems that may lead to unexpected results.Specifically,they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images,resulting in incorrect license plate recognition.There are some classic methods to generate adversarial examples,but they cannot be adopted on LPRS directly.In this paper,we modify some classic methods to generate adversarial examples that could mislead the LPRS.We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples.In addition,we show that the generated images could also attack the black-box systems;we show some examples that the Baidu LPR system also makes incorrect recognitions.We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks.

Research and Implementation for License Plate Recognition Based on Improved Projection Algorithm

This paper analyzes and dissertates the discrete wavelet transform and improved projection algorithm in four kernel stages （image preprocessing, license plate localization, character segmentation, license plate recognition, i.e.） of license plate recognition system in detail. At last, it gives some conclusions and suggestions on future research.

PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure.To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective characteristics.Subsequently,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online.The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.

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

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.

Traffic control optimization strategy based on license plate recognition data

Traffic signal control is essential to the efficiency of the road network’s operation.In recent years,more and more detailed detection data provide potential data support for traffic signal control,such as license plate recognition(LPR)data.This study aims to develop a traffic signal control optimization method based on model predictive control(MPC)and LPR data.The proposed framework of a closed-loop control system is described in detail.First,the control objectives and queue prediction model for signalized intersection are determined.Then,online optimization and feedback compensation are discussed and implemented.Calculations of the arrival rate at the downstream are based on the LPR data detected at the upstream intersection,and dynamic optimization method of the offset is proposed for a coordinated control.The model is validated using the LPR data of two consecutive intersections with a traffic simulation platform.Results demonstrate that the model can restrain extreme long queuing,improve intersection capacity,and reduce intersection average delay.The developed model promotes the system operating efficiency and shows the general advantage of real-time optimization,feedback,and control.The proposed framework can be potentially applied by local traffic management centers to improve the quality of traffic signal control.

Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

Deep Learning Based License Plate Number Recognition for Smart Cities

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

Libyan Licenses Plate Recognition Using Template Matching Method

License plate recognition (LPR) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. The work presented in this paper aims to create a computer vision system capable of taking real-time input image from a static camera and identifying the license plate from extracted image. This problem is examined in two stages: First the license plate region detection and extraction from background and plate segmentation to sub-images, and second the character recognition stage. The method used for the license plate region detection is based on the assumption that the license plate area is a high concentration of smaller details, making it a region of high intensity of edges. The Sobel filter and their vertical and horizontal projections are used to identify the plate region. The result of testing this stage was an accuracy of 67.5%. The final stage of the LPR system is optical character recognition (OCR). The method adopted for this stage is based on template matching using correlation. Testing the performance of OCR resulted in an overall recognition rate of 87.76%.

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system

In this paper, the performance of you only look once(YOLO) series detectors on Chinese license plate recognition(LPR) in the real intelligent transportation system(ITS) monitoring scene is investigated. Specially, a precise and efficient automatic license plate recognition(ALPR) system based on the YOLOv4 detector is proposed. The proposed ALPR system contains three stages including vehicle detection, license plate detection(LPD) and LPR. In vehicle detection stage, YOLOv4 detector is directly applied. In LPD stage, YOLOv4-tiny detector is exploited. In the last stage, the YOLOv4-tiny detector with attention mechanism for LPR is proposed to use. In addition, a large Chinese license plate dataset containing 10 500 images collected from all 31 provinces in the Chinese mainland is created. This Chinese license plate dataset is named Hefei University of Technology license plate version 1(HFUT-LP v1). Particularly, HFUT-LP v1 dataset is collected in the real ITS monitoring scene. In order to compare the performance of different object detection algorithms for ALPR, a variety of object detection algorithms are used to make a comprehensive performance evaluation. Experimental results show that the proposed ALPR system achieves very high accuracy and has very fast processing speed, which is suitable for real-time LPR.

A Practical Method of Car License Plate Character Segmentation Based on Morphology and Labeling

In this paper, a kind of practical image segmentation algorithm for segment characters from car license plate is presented, based on morphology and labeling. First by morphological operation, noise in the binary image of license plate can be greatly decreased. Then, by labeling, each connected pixel component is given a unique label. Finally, by the known data of license plate, each character is extracted correctly. The advantage of this method is that it can deal with plates with different sizes and connected characters plates, and inclined plates. The experiment results show that it is an effective way to extract characters from the license plate, and can be put into practical use.

Implementing VLPR systems based on TMS320DM642

This paper gives a practical schema for using DSP boards to construct Vehicle License Plate Recognition (VLPR) modules that could be embedded in any Intelligent Transportation System (ITS). Using DSP can avoid the heavy investment in dedicated VLPR system and improve the computational power compared to PC software environment. Low cost, high computational power, and high flexibility of DSP provide the License Plate Recognition System (LPRS) an excellent cost-effective solution to execute the major part of the recognition tasks. This paper describes a successful implementation of VLPR system based on Texas Instruments (TI)'s TMS320DM642. The DSP board acquires video (which could be output to a monitor for surveillance) from a camera, captures images from the video, locates and recognizes the license plates in images, and then sends the recognized results and related images after compression to a host PC through the network. Finally, the overall software is optimized according to the features of DM642 chip. Experiments showed that the DSP VLPR system performs well on the local license plates, and that the processing speed and accuracy can meet the requirement of practical applications.

Uncovering the CO_(2)emissions of vehicles:A well-to-wheel approach

Carbon dioxide(CO_(2))from road traffic is a non-negligible part of global greenhouse gas(GHG)emissions,and it is a challenge for the world today to accurately estimate road traffic CO_(2)emissions and formulate effective emission reduction policies.Current emission inventories for vehicles have either low-resolution,or limited coverage,and they have not adequately focused on the CO_(2)emission produced by new energy vehicles(NEV)considering fuel life cycle.To fill the research gap,this paper proposed a framework of a high-resolution well-to-wheel(WTW)CO_(2)emission estimation for a full sample of vehicles and revealed the unique CO_(2)emission characteristics of different categories of vehicles combined with vehicle behavior.Based on this,the spatiotemporal characteristics and influencing factors of CO_(2)emissions were analyzed with the geographical and temporal weighted regression(GTWR)model.Finally,the CO_(2)emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies.The results show that the distribution of vehicle CO_(2)emissions shows obvious heterogeneity in time,space,and vehicle category.By simply adjusting the existing NEV promotion policy,the emission reduction effect can be improved by 6.5%-13.5%under the same NEV penetration.If combined with changes in power generation structure,it can further release the emission reduction potential of NEVs,which can reduce the current CO_(2)emissions by 78.1%in the optimal scenario.