License Plate Location Research Articles

Automatic Traffic E-challan Generation

Utilizing state-of-the-art technology, Automated Traffic E-challan Generation systems are employed to promptly detect and document traffic violations as they occur. By seamlessly integrating cameras, sensors, and sophisticated algorithms, these systems independently identify transgressions including but not limited to speeding and unauthorized parking. After detection, electronic citations (e-challans) are swiftly produced and delivered to vehicle proprietors, thereby optimizing enforcement procedures and bolstering overall road safety. However, notwithstanding their efficacy, the deployment of such systems mandates the meticulous consideration of privacy and equity issues, necessitating the enactment of suitable regulatory protocols. The Automatic recognition of license plate is the basis of effective management in traffic, the automatic detection and localization of license plate is an important part. Keywords- Python, Raspberry Pi, Convolutional Neural Network

Docker Container-Based Framework of Apache Kafka Node Ecosystem: Vehicle Tracking System by License Plate Recognition on Surveillance Camera Feeds

Vehicle tracking and license plate recognition (LPR) over video surveillance cameras are essential intelligent traffic monitoring systems. Due to the enormous amount of data collected each day, it would be difficult to track vehicles by license plate in a real-world traffic setting. Large volumes of data processing, real-time request responses, and emergency scenario response may not be possible using conventional approaches. By combining license plate recognition with the docker container-based structure of the Apache Kafka node ecosystem, the suggested solution takes a novel approach to vehicle tracking. The primary components of our suggested framework for reading license plates are the identification of license plates and text data queries. License plate localization is performed with You Only Look Once version 3 (YOLOv3) and character recognition with Optical Character Recognition (OCR). The detected vehicle images with license plate results are published on related topics with Apache Kafka. Apache Kafka is a publish-subscribe (producers-consumers) messaging system and one of the most popular architectures used for streaming data. For each license plate search, a topic will be created in the framework where producers publish and consumers receive data. Thus, the workload of the operators will be reduced and they will be able to pay attention to more important events in traffic.

Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network

To solve the problem of reverse car searching in intelligent multi-story garages or parking lots, the reverse car searching method based on the intelligent garage of the PC client and mobile client APP was studied, and the interface design and function development of the system’s PC and mobile client APP were carried out. YOLOv5 network and LPRNet network were used for license plate location and recognition to realize parking and entry detection. The indoor pedestrian location method based on RSSI fingerprint signal fusion BPNet network and KNN algorithm was studied, and the location accuracy within 2.5 m was found to be 100%. The research on the A* algorithm based on spatial accessibility was conducted to realize the reverse car search function. The research results indicate that the guidance of the vehicle finding path can be completed while the number of invalid search nodes for the example maps was reduced by more than 55.0%, and the operating efficiency of the algorithm increased to 28.5%.

An Efficient Deep Learning Approach for Automatic License Plate Detection with Novel Feature Extraction

In the domain of traffic management, road toll collection, and parking lot systems, vehicle number plate detection and identification play a pivotal role. Unlike conventional methods that treat license plate detection and character recognition as separate tasks, the system simultaneously addresses both challenges within a single neural network. Our Proposed methodology capitalizes on the efficiency and accuracy of the one-stage object detection algorithm known as YOLO (You Only Look Once) to locate license plates under diverse and challenging conditions. To augment the quality of input images with low resolution or poor clarity, we employ super-resolution generative adversarial networks (SRGANs). The image enhancement process substantially improves the visual quality of captured license plate images, facilitating more precise character recognition. Quantitative assessment of propounded system reveals compelling results. The license plate detection component achieves an outstanding average accuracy rate of 98.5%, surpassing previous methods by 15.2%. This comprehensive approach not only reduces the dependency on manual labour but also elevates processing precision. It seamlessly integrates into existing transportation infrastructure, resulting in heightened operational efficiency, reduced traffic congestion, and enhanced security measures. The rapid evolution of neural networks and deep learning techniques has streamlined the deployment of such applications, revolutionizing the field of traffic monitoring and management with unprecedented ease and precision. One-stage object detector, widely referred to as YOLO, is used to find licence plates in difficult circumstances.

Development analysis and future of computer vision-based automobile license plate recognition technology and perception technology

With the increasing number of automobiles, there is a growing demand for the recognition and perception of license plates. As a unique identifier for each vehicle, license plates can help traffic management departments with vehicle tracking, real-time monitoring, and other tasks. License plate recognition and perception methods based on these technologies have been widely used. This paper aims to analyze and summarize the development of computer vision-based license plate recognition technology. The paper mainly discusses the two key steps in the license plate recognition process: license plate localization and detection, and character segmentation and recognition. These steps are classified into traditional methods and deep learning-based methods, and several methods are introduced for each category. Finally, by summarizing their advanced features and limitations, the paper compares them and predicts possible ways to improve them in the future.

LOKALISASI PLAT NOMOR DENGAN JAVA MENGGUNAKAN DETEKSI TEPI CANNY DAN FILTER MORFOLOGI

This study aims to introduce an automatic vehicle identification system based on vehicle licenses and license plate recognition. This Automatic Number Plate Recognition (ANPR) system software uses a series of image processing algorithms captured by a camera with a high degree of accuracy and has the efficiency to recognize license plates and then identify vehicles from datasets stored on a Personal Computer (PC). This study uses an algorithm for license plate localization by implementing the Canny edge detection algorithm and a morphological filter.The methodology used is to develop a Number Plate (NP) localization method including the following steps: (1) Color conversion, image enhancement (using sharpness filters and gamma correction), Canny edge detection and alignment (skeletonization); stage (2) morphological filter-based method to determine the Region of Interest (ROI), namely a rectangular box containing a Number Plate (NP). A rectangular bounding box is drawn across the area in the original image that contains the NP after the NP are localized. The bounding box is equal to the width of the image plus one pixel and the height of the NP plus one pixel.The results showed that this method succeeded in achieving a localization rate of about 72.0%. The method used for license plate localization is able to localize license plates with clear boundaries and unambiguous separation of other related components. This research can be further developed to include more functions so that it can be used by all elements of society from researchers to law enforcers.

Parallelization of license plate localization on GPU platform

Parallelization of license plate localization on GPU platform

A single-stage automatic license plate recognition network with Balanced-IoU loss

In this paper, we propose a license plate recognition model, which can detect and recognize the license plate in a single forward. The features of the input image are extracted by our 15-layer convolutional neural network. In detection branch, we use a loss function with better nonlinear to fit the detection process of license plate. To catch the location of license plate with less information loss, we add Intersection over Ground-truth (IoG) into the Intersection over Union (IoU) and get Balanced-IoU loss (BIoU loss). The combination of these two loss functions can make the model get a better predictive result. In recognition branch, we introduce an attention mechanism to make better recognition results. The experimental results show that our network can get 99.91% recognition precision, which is over 3% higher than state-of-the-art single-stage ALPR network RPnet.

Vehicle Number Plate Detection

This study introduces an enhanced version of a previously published genetic algorithm based technique to allow fast and accurate detection of the vehicle plate number independently of the used application. This paper is dedicated on an improved technique of OCR based license plate recognition using neural network trained dataset of object features. A blended algorithm for recognition of license plate is proposed and is compared with existing methods for improve accuracy. The whole system can be categorized under three major modules, namely License Plate Localization, Plate Character Segmentation, and Plate Character Recognition. The system is simulated on 300 national and international motor vehicle LP images and results obtained justifies the main requirement

Two-Step Algorithm for License Plate Identification Using Deep Neural Networks

License plate identification remains a crucial problem in computer vision, particularly in complex environments where license plates may be confused with road signs, billboards, and other objects. This paper proposes a solution by modifying the standard car–license plate–letter detection approach into a preliminary license plate detection–precise license plate detection of the four corners where the numbers are located–license plate correction–letter identification. This way, the first algorithm identifies all potential license plates and passes them as input parameters to the next algorithm for more precise detection. The main difference between this approach and other algorithms is that it uses a relatively small image compared to the whole vehicle. Thus, a small but robust network is used to find the four corners and perform a perspective transformation. This simplifies the letter recognition task for the next algorithm, as no additional transformations are required. This solution could be useful for research focusing on this specific task. It allows to apply another compact but robust neural network, increasing the overall speed of the system. Publicly available datasets were used for training and validation. The CenterNet object detection algorithm was used as a basis with a modified Hourglass-type network. The size of the network was decreased by 40% and the average accuracy was 96.19%. Speed significantly increased, reaching 2.71 ms and 405 FPS on average.

Vehicle Instance Segmentation Polygonal Dataset for a Private Surveillance System

Vehicle identification and re-identification is an essential tool for traffic surveillance. However, with cameras at every corner of the street, there is a requirement for private surveillance. Automated surveillance can be achieved through computer vision tasks such as segmentation of the vehicle, classification of the make and model of the vehicle and license plate detection. To achieve a unique representation of every vehicle on the road with just the region of interest extracted, instance segmentation is applied. With the frontal part of the vehicle segmented for privacy, the vehicle make is identified along with the license plate. To achieve this, a dataset is annotated with a polygonal bounding box of its frontal region and license plate localization. State-of-the-art methods, maskRCNN, is utilized to identify the best performing model. Further, data augmentation using multiple techniques is evaluated for better generalization of the dataset. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches on the same dataset. A classification accuracy of 99.2% was obtained and segmentation was achieved with a high mAP of 99.67%. Data augmentation approaches were employed to balance and generalize the dataset of which the mosaic-tiled approach produced higher accuracy.

Chinese License Plate Recognition System Based on Convolutional Neural Network

License Plate Recognition (LPR) technology has been developed for many years, but for the time being, LPR technology still has problems such as not being accurate enough in positioning and too long recognition time. Especially in China, the License Plate (LP) is made up of Chinese characters, alphabets, and numbers, in which the use of Chinese characters profoundly influences the accuracy of LPR. In this paper, a Convolutional Neural Network (CNN) is employed in LPR system, and the LPR system designed in this paper includes three parts: coarse LP positioning, precise LP positioning, and LP character recognition. The LPR system designed in this paper uses the functions in OpenCV to call the laptop camera and input the obtained images into the computer to achieve interframe recognition by setting the timer. Once the image is acquired, the approximate license plate position is determined by the cascade classifier. After this, the image is processed by adaptive binarization, followed by Connected Component Analysis (CCA), linear fitting, and left-right regression to precisely locate the LP. After the exact location of the LP has been determined, it will be fed into a CNN for character recognition. Simulation tests show that the LPR system implemented in this paper has some LPR capability.

Video Sensor-Based Automatic License Plate Recognition of Static and Moving Vehicles

In Bangladesh, present transportation system needs suitable control schemes to ensure safety and mobility to the road users. License Plate Recognition (LPR) is one of the control schemes which can be used to ensure road safety using video sensors. Moreover, from installation point of view, it is cheaper than Radiofrequency Identification (RFID) system. Automated LPR is adopted to reduce human involvement which causes loss in accuracy and reliability in reading license plate. The extracted information from LPR can be used in various schemes, such as electronic payment system, traffic surveillance and extracting vehicle trajectory. In this paper, we are presenting an offline dynamic LPR system, which uses geometric properties of Bengali alphabets stored in alphabet template database to recognize license plates from video. The system comprises of three main components: 1) License Plate Locating (LPL), 2) Character Segmentation (CS), and 3) Geometric Character Recognition (GCR). In this regard, the image is converted into a binary image considering morphological operations and color modification. A new ratio-based detection algorithm has been proposed for the segmentation of license plate. Finally, the alphabet template database and the character extracted from detected license plate are correlated to recognize alphanumeric character which deduces the plate number. Thus, the plate number can be utilized for further action.

License Plate Recognition System Based on Improved YOLOv5 and GRU

Aiming at the problem that the traditional license plate recognition method lacking of accuracy and speed, an end-to-end deep learning model for license plate location and recognition in natural scenarios was proposed. First, we added an improved channel attention mechanism to the down-sampling process of the You only look once(YOLOv5). Additionally, a location information is added in the ones to minimize the information loss from sampling, which can improve the feature extraction ability of the model. Then we reduce the number of parameters on the input side and set only one class in the YOLO layer, which improves the efficiency and accuracy of the detector for locating license plates. Finally, Gated recurrent units(GRU) + Connectionist temporal classification(CTC) was used to build the recognition network to complete the character segmentation-free recognition task of the license plate, significantly shortened the training time and improved the convergence speed and recognition accuracy of the network. The experimental results show that the average recognition precision of the license plate recognition model proposed in this paper reaches 98.98%, which is significantly better than the traditional recognition algorithm, and the recognition effect is good in complex environment with good stability and robustness.

Automatic Localization of License Plate for Car in Wolfram Mathematica

Modern imaging devices make it possible to solve a complex of technical applied problems that require the synthesis and analysis of computer processing methods using threshold binarization, image classification, clustering, and the use of machine learning to determine areas of interest. Thus, segmentation algorithms are widely used for processing medical images. Computer technologies are used for the functioning of the intellectual environment, which allows to analyze the state of human health. The development of microelectronics makes it possible to increase the complexity of the applied image processing algorithms used to solve applied engineering problems. The issues of segmentation, pattern recognition, description and presentation of details, morphological analysis of images obtained by industrial equipment are widely discussed in the literature. For example, theories of optical signal processing taking into account interference, issues of image perception and analysis are presented in detail in domestic and foreign literature. The paper describes the developed algorithm for localizing a car license plate, implemented in the Wolfram Mathematica system. First, the region of interest is determined, isolated from the rest of the image for its subsequent processing. An image representation is implemented using an affine transformation. Further segmentation of the characters on the license plate allows the characters to be identified. In the Mathematica system, a program code for the car license plate localization algorithm for its further recognition has been developed. The solution to the problem was obtained using the step-by-step application of the built-in and user-defined functions of the Wolfram Mathematica system. The algorithm has been tested on a representative sample of images. The average error did not exceed 10 %, which is in line with modern industrial image processing algorithms. The resulting car license plate identification algorithm can be used in digital devices to automatically determine and further image processing.

License plate detection using AI

In today's world, withincreasing population number of vehiclesare also increased. Monitoring each andevery vehicle in roadside by manualforce becomes a tedious job. For effective traffic management some solution is required. The traditional system of licenseplate recognition basically relies on themorphological processing of images. The accuracy of recognition is also lower. Inorder to provide an efficient solution forlicense plate position, we propose a new method of license plate recognition. In the license plate position, we use traditional positioning method and Haar Cascade algorithm to detect the licenseplate. In addition with the traditionalidentification system cutting and matching, we use the ability feature extraction of Convolution Neural Network (CNN) to detect the license platedirectly, which avoids the recognitionerror which is caused by the segmentation in the license platerecognition. In this project, we propose a new license plate recognition system. It is divided into two parts, they are licenseplate positioning and characterrecognition.

Research on license plate location and recognition in complex environment

Research on license plate location and recognition in complex environment

Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Intelligent transportation applications based on urban Internet of Things can improve the efficiency of government services and promote urban modernization. As smart cameras are more and more widely used in cities, artificial intelligence technology is an important force to achieve license plate recognition. An efficient license plate recognition algorithm not only improves the efficiency of traffic management but also saves management costs. This paper proposes a network based on the shufflenetv2 dilated convolution (SDC) model, which includes two parts: license plate location and license plate recognition. SDC model adopts shufflenetv2 as the backbone network, which combines dilated convolution and global context blocks. Therefore, the receptive field and feature expression ability of the model are enhanced. For license plate location, CIOU loss considers not only the coverage area of the bounding box but also the center distance and aspect ratio. For license plate recognition, CTC loss trains the network based on the sequence and solves the sample alignment problem, which improves the accuracy of license plate recognition. The experiments show that the precision of the SDC model in license plate location is 98.7%, which is 5.2%, 5.5%, and 4.1% higher than the precision of Faster-RCNN, YOLOv3, and SSD, respectively. The precision of the SDC model in license plate recognition is 98.2%, which is 5.3%, 3.7%, and 2.9% higher than the precision of LPRNet, AlexNet, and RPNet, respectively.

Research on License Plate Location Algorithm Based on YOLOv5

License plate location is a key part of license plate recognition, and its positioning accuracy seriously determines the final result of license plate recognition. Firstly, the traditional license plate location methods are compared and analyzed. Secondly, in order to solve the localization problem in low resolution and multi-vehicle environment, a license plate method based on YOLOv5s was proposed by using deep learning image recognition technology, and data enhancement was introduced to improve YOLOv5s. Finally, the YOLOv5 algorithm is applied to real vehicle images under complex environmental conditions for experimental verification, and the results show that the accuracy is 99.12%. The experimental results show that the license plate location model based on YOLOv5S can effectively solve the influence of illumination, image quality and other factors on license plate location, improve the efficiency and accuracy compared with the traditional license plate location method, the algorithm has good robustness and fast calculation speed.

Road geometry perception without accurate positioning and lane information

Road geometry perception indicates road direction and drivable area for Lane Keeping System and Lane Departure Warning System in Advanced Driver Assistance Systems or routing and decision-making in Autonomous Driving. High-definition map with accurate positioning or lane information is generally required. However, these two types of information are not always available because of positioning failure or lane mark degradation. In this paper, a road geometry estimation method is proposed without requirement of accurate positioning and lane information. Due to the fact the lane guides the front vehicle, the potential correlation between the relative azimuth of front vehicle and the direction of road is explored. The stable and observable information of front vehicles are exploited, such as the license plate location. A radar-camera fusion system using image processing, and a nonlinear system is then built with Unscented Kalman Filter to estimate the road curvature. Simulation results with Carla demonstrate that the method enhances the road geometry perception by 77.1%, compared with the baseline method. The proposed method can provide reliable road geometry information for autonomous driving when accurate positioning and lane information are unavailable, so that it renders more robust and safer perception for future autonomous driving.