License Plate Detection System Research Articles

LICENSE PLATE RECOGNITION SYSTEM BASED ON MASK R-CNN

Automatic license plate recognition (ALPR) systems can be found in many different applications. If a person has a driving license, the person probably had already seen smart road camera after a speed limit sign or on a crossroad. Every year number of cars on roads growth very fast. It is also obvious that such systems can be used out-of-road situations. For instance, this type of systems can be used for automatic access control on private property or smart parking, or even log system that are being used literally everywhere. Because of popularity of ALPR systems, there are two main goals, which are being pursued by researches: speed and accuracy of recognition. Speed of the detection is important for real-time systems. Accuracy is important for every system. The more accurate a system is, the more reliable it is. For example, car accident detection systems should be as accurate as possible in order to be used, because no one wants to get billed with the wrongdoing, that wasn’t committed by them.
 The purpose of the study is to develop high precision automatic license plate detection system with number extraction possibilities. In order to achieve the goal many different modern solutions and technologies were studied and solution is presented. The main technology of the project is artificial intelligence system and, more specifically, convolutional neural network. As the main algorithm Mask R-CNN is used for license plate detection. To present reasonable research, the system was tested on different hardware (CPU, GPU, Raspberry PI 4) and on different datasets.

YOLO Based Advanced Smart Traffic Assistance Platform for Number Plate and Helmet Detection

Abstract: Now a days road accidents are one of the major causes that are leading to human death. However the most common reason for motorcycle deaths is because many fail to confirm to the law of wearing helmet. Here is the software using YOLO V8 to recognize the motorbike drivers , who are not obeying helmet law in an automated way. The helmet and license plate detection system using YOLO V8 is a computer vision technology-based system that utilizesthe You Only Look Once (YOLO) objectdetection algorithm to detect helmets and license plates in real-time. The system is designed to improve safety on roads and highways by detecting riders without helmets and vehicles without properlicense plates. The system consists of motorcycledetection , helmet and no helmet detection as well as bike license plate recognition. The system is capable of processing image from a variety of sources, including trafficcameras and drones, and can detect the presence or absence of helmets and license plates in the image frames. It uses a deep learning model trained on a large dataset of annotated images to identify and classify objects. The output of the system includes a bounding box around each detected object and a label indicating whether it is a helmet or a license plate. The system can also be configured to generate alerts or notifications when violations are detected. Overall, this system provides a valuable tool for law.

Implementation of number plate detection system for vehicle registration using IOT and recognition using CNN

In the intelligent transportation system the automatic license plate recognition and detection plays a very important role. This application could be used for traffic control security e-payment systems in the toll pay and parking. Many algorithms have been developed to force license plate detection and recognition and all have many advantages and flaws under different situations. With the advent and rise of deep learning concepts in various fields of artificial intelligence, computer vision has developed in leaps and bounds in terms of innovations and methods. Automatic License Plate Recognition has emerged as an effective method to automate the watch keeping process for traffic systems, parking fee structures, and police surveillance. License plate recognition (LPR) is a quite used and mature technology but much work is needed to be done in order to make it perfect. In recent years, the scientific community has made major advances in methodology and performance. This paper tries to aim at summarizing and analyzing various methodologies and progress in LPR in the deep learning era using IOT sensors. Hence, in this paper, an Automatic License Plate Detection and Recognition (ALPDR) system has been proposed having four steps namely License Plate Extraction, Image Pre-processing, Character Segmentation and Character Recognition. For the first three steps (extraction, pre-processing, and segmentation), unique methods have been proposed. As the character recognition is an important step of license plate recognition and detection, four different methods for character recognition have been experimented on, which include Convolution Neural Network (CNN), MobileNet, Inception V3, ResNet 50.

Moving Vehicle Registration Plate Detection

Abstract: License plate detection and E-challan system is an automated solution for traffic law enforcement that utilizes cameras, image processing algorithms, and software to detect license plates of vehicles and issue electronic challans for traffic rule violations. The system involves the installation of cameras at strategic locations on roads and highways, which capture images of passing vehicles. The E-challan system is an efficient and accurate system that reduces the workload of traffic police and enablesquick identification and penalization of traffic violators. E-Challan System is the online platform aimed at providing a wide rangeof support in managing and monitoring the traffic penalties, helping users regarding the problems they face in paying for their challan. The E-challan System is basically an interaction between Police and drivers easily through an online platform or an app. This project prototype describes how challan becomes easy for users through keeping it online. The online platform aims to reducethe paperwork, manual process and increase the convenience for the users

Research on License Plate Detection and Recognition System based on YOLOv7 and LPRNet

With the development and continuous iteration of digital transformation and artificial intelligence technology, the license plate detection and recognition system based on traditional machine vision and the current deep learning-based license plate recognition system for China is unable to meet the needs of rapid and accurate real-time recognition and recognition in complex environments. This paper designs and integrates a set of license plate detection and recognition system based on YOLOv7, STN and LPRNet models, which can recognize Chinese license plates quickly and accurately in real time, and has good robustness in complex environment. Its average accuracy in complex environments reached 96.1%, indicating that the system has a better effect than the traditional license plate detection and recognition system.

LSV-LP: Large-Scale Video-Based License Plate Detection and Recognition.

In the past few decades, license plate detection and recognition (LPDR) systems have made great strides relying on Convolutional Neural Networks (CNN). However, these methods are evaluated on small and non-representative datasets that perform poorly in complex natural scenes. Besides, most of existing license plate datasets are based on a single image, while the information source in the actual application of license plates is frequently based on video. The mainstream algorithms also ignore the dynamic clue between consecutive frames in the video, which makes the LPDR system have a lot of room for improvement. In order to solve these problems, this paper constructs a large-scale video-based license plate dataset named LSV-LP, which consists of 1,402 videos, 401,347 frames and 364,607 annotated license plates. Compared with other data sets, LSV-LP has stronger diversity, and at the same time, it has multiple sources due to different collection methods. There may be multiple license plates in a frame, which is more in line with complex natural scenes. Based on the proposed dataset, we further design a new framework that explores the information between adjacent frames, called MFLPR-Net. In addition to these, we release the annotation tools for license plates or vehicles in videos. By evaluating the performance of MFLPR-Net and some mainstream methods, it is proved that the proposed model is superior to other LPDR systems.In order to be more intuitive, we put some samples on https://drive.google.com/file/d/1udqRddpJZMpTdHHQdwZRll6vaYALUiql/view?usp=sharingGoogle Drive. The whole dataset is available at https://github.com/Forest-art/LSV-LP.

A sustainable competing dynamic - Real-time Bangla license plate detection and recognition system using YOLOv5 and SSD: A deep learning application

In this day and age, the programmed procurement of a tag and acknowledgment assumes a significant part in observing and coordinating vehicles in significant urban communities. It is hard to recognize a driver or proprietor of a vehicle that disregards traffic controls or plays out any incidental movement out and about. It will require a great deal of investment for a cop to review the plate of every vehicle. Subsequently, a mechanized tag acknowledgment framework can tackle these sorts of issues. This is the proposed technique; two Deep Learning calculations are utilized to distinguish the permit number and characters on the tag from the constant picture. The primary YOLOv5 model tracks down the main in the live video of a vehicle out and about. Then, at that point, cut out the area of the permit numbers in the video. The cut casing is then embedded into a second SSD (Single Shot Detection) to identify slugs on that tag. The prepared model acquires a high precision of 96.2% over a sum of 400 picture databases.

Development of a Counterfeit Vehicle License Plate Detection System by Using Deep Learning

In this study, a deep learning-based counterfeit plate detection system that compares and detects vehicles with the make, model, color, and license plate is designed. As known that the relevant government institutions are responsible for keeping all detailed information about all motor vehicles in their database. All registration details are stored in the database. It is possible to find unregistered vehicles by comparing database records with detected details. In general, vehicles with counterfeit license plates are used in illegal actions. Therefore, it is of great importance to detect them. Generally, license plate recognition systems successfully detect counterfeit license plates that are randomly generated. Security units typically use such systems at toll roads, bridge crossings, parking lot entrances and exits, sites, customs gates, etc. This kind of system only checks the plate is exists or not in the database. But it is unsuccessful if the vehicle uses existing plate numbers such as stolen ones. In this study, the developed system can detect not only vehicles' plate numbers but also make, model, year, and color information by using deep learning. Thus, the system can also detect randomly generated plates and stolen plates that belong to another vehicle.

A Robust License Plate Detection and Recognition System Based on Detr and Cnn

A Robust License Plate Detection and Recognition System Based on Detr and Cnn

Real-time Automated Detection and Recognition of Nigerian License Plates via Deep Learning Single Shot Detection and Optical Character Recognition

License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.

Indonesian License Plate Detection and Identification Using Deep Learning

Abstract— License plate is the unique identity of the vehicle, which serves as proof of the legitimacy of the operation of the vehicle in the form of a plate or other material with certain specifications issued by the police and contains the area code, registration number and validity period and installed on the vehicle. License plates are often used in automated parking systems and vehicle identification in case of traffic violations. So, it is necessary to build a system for detection and identification of license plates. The proposed license plate detection and identification system is divided into three main processes, namely license plate detection, character segmentation, and character recognition. The detection process uses transfer learning techniques using Faster R-CNN Inception V2. The segmentation process uses traditional computer vision with morphological operations and contours extraction. Then the character recognition process uses the MobileNet V2 transfer learning technique as an architecture for character classification. The recognition accuracy compared between MobileNet V2 and TesseractOCR shows that MobileNet V2 is superior with an accuracy rate of 96%, while Tesseract-OCR has a poor accuracy of 59%. Keywords— Deep Learning, Convolutional Neural Network, License Plate Detection, Character Segmentation, Character Recognition

An automated license plate detection and recognition system based on wavelet decomposition and CNN

The License Plate detection and recognition (LPDR) is a challenging task that plays a significant role in intelligent transportation systems (ITS). Where it could be used as a core in various applications, such as security, traffic control, and electronic payment systems (e.g. freeway toll payment and parking fee payment). A variety of algorithms are developed for this work and each one has advantages and disadvantages for extracting plates in images under different circumstances. However, the complexity of some methods requires a high calculation cost and this could be time-consuming. In the current paper, a simple and efficient method is proposed to tackle the issue of license plate detection and character recognition. The license plate is detected first based on the two-dimensional wavelet transform to extract the vertical edges of the input image. The high density of vertical edges is computed first to detect the potential areas of the license plate. Then these potential areas are verified by using a plate/non-plate CNN classifier. After the license plate is detected, the characters are segmented by using a simple method that is based on the empty distance between the characters. Finally, these character candidates are classified by training another CNN classifier. The experiments were done on vehicles that carry Moroccan license plates and showed high accuracy, where the results obtained go up to 99.43% in term of localization and 98.9% in term of recognition. Besides, the efficiency and the high accuracy of the proposed method were proved by performing a comparison with other works from the literature on different datasets. All processes of the proposed method were implemented on a Hardware Processor System (HPS) located in a VEEK-MT2S provided by TERASIC.

A robust method to authenticate car license plates using segmentation and ROI based approach

Purpose The purpose of this paper is to explore the challenges faced by the automatic recognition systems over the conventional systems by implementing a novel approach for detecting and recognizing the vehicle license plates in order to increase the security of the vehicles. This will also increase the societal discipline among vehicle users. Design/methodology/approach From a methodological point of view, the proposed system works in three phases which includes the pre-processing of the input image from the database, applying segmentation to the processed image, and finally extracting and recognizing the image of the license plate. Findings The proposed paper provides an analysis that demonstrates the correctness of the algorithm to correctly capture the license plate using performance metrics such as detection rate and false positive rate. The obtained results demonstrate that the proposed algorithm detects vehicle license plates and provides detection rate of 93.34 percent with false positive rate of 6.65 percent. Research limitations/implications The proposed license plate detection system eliminates the need of manually used systems for managing the traffic by installing the toll-booths on freeways and bridges. The design implemented in this paper attempts to capture the license plate by using three phase detection process that helps to increase the level of security and contribute in making a sustainable city. Originality/value This paper presents a distinctive approach to detect the license plate of the vehicles using the various image processing techniques such as dilation, grey-scale conversion, edge processing, etc. and finding the region of interest of the segmented image to capture the license plate of the vehicles.

Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform

The realization of a deep neural architecture on a mobile platform is challenging, but can open up a number of possibilities for visual analysis applications. A neural network can be realized on a mobile platform by exploiting the computational power of the embedded GPU and simplifying the flow of a neural architecture trained on the desktop workstation or a GPU server. This paper presents an embedded platform-based Italian license plate detection and recognition system using deep neural classifiers. In this work, trained parameters of a highly precise automatic license plate recognition (ALPR) system are imported and used to replicate the same neural classifiers on a Nvidia Shield K1 tablet. A CUDA-based framework is used to realize these neural networks. The flow of the trained architecture is simplified to perform the license plate recognition in real-time. Results show that the tasks of plate and character detection and localization can be performed in real-time on a mobile platform by simplifying the flow of the trained architecture. However, the accuracy of the simplified architecture would be decreased accordingly.

Ensemble of adaboost cascades of 3L-LBPs classifiers for license plates detection with low quality images

Due to the plate formats and multiform outdoor illumination conditions during the image acquisition phase, it is challenging to find effective license plate detection (LPD) method. This paper aims to develop a new detection method for identifying vehicle license plates under low quality images using image processing techniques. In this research, a robust method using a large number of AdaBoost cascades with three levels pre-processing local binary patterns classifiers (3L-LBPs) are used to detect license plates (LPs) regions. The method achieves a very high accuracy for detecting LP number from one vehicle image. The proposed method was tested and trained with the images from 630 and 400 vehicles, respectively. The images involve many difficult conditions, such as low/high contrast, dusk, dirt, fogy, and distortion problems. The experimental results demonstrate very satisfactory performance for LP detection in term of speed and accuracy, and were better than the most of the existing methods. The processing time for the whole testing LPD system was about 1.63 seconds to 2 seconds. The overall probability detection, precision, and f-measurement are 98.56%, 95.9% and 97.19%, respectively; with false positive rate 5.6%.

Automatic Car License Plate Detection System for Odd and Even Series

Automatic Car License Plate Detection System for Odd and Even Series

Vehicle License Plate Detection and Recognition System

This is a survey paper on the Vehicle License Plate Detection and Recognition System. This system works in five phases. The first phase is the input given to the system which is an image of the vehicle with clearly visible license plate. The second phase is the localization of the license plate from the image. The third phase is the segmentation of the characters in the localized license plate. In the fourth phase the segmented characters are recognized using Artificial Neural Networks. In the fifth and final phase these recognized characters are stored in a database for further reference. General Terms Automatic License Plate Recognition (ALPR), MATLAB, Neural Networks

Iraqi Cars License Plate Detection and Recognition System using Edge Detection and Template Matching Correlation

Automatic License Plate Detection and Recognition (ALPDR) system is an image processing technology used to identify cars by their license plates.This system consists of two main parts: the first is a practical implementation of how to take an automatically snapshotfor cars passes, this was done by two sensors type(GP2Y0A21YK0F sensor) connected with each other and interfaced with a A4tech USB camera,and this camera is interfaced using Matlab 2013a with the personal computer. The second part is image processing, this part includes four basic stages: The first stage is image preprocessing which involves image normalization and RGB to gray image conversion. Second stage is detection of a possible license plate using edge detection technique and extracts these LP using region growing technique, detection rates reach to 95%. Third stage is alphanumeric character segmentation to isolate each character, numbers and words of the license plate using Otsu's and Hough transforms technique for subsequent recognition. The last stage reads the alphanumeric character and words by correlation template matching, which is a simple, fast and given a recognition rate reach to 98.245%.

Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Most of the existing license plate (LP) detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases.

Car License Plate Detection

paper deals with car license plate detection (CLPD) system in order to identify vehicles by capturing their car license plates (CLP). Car license plate detection (CLPDS) is an emerging area of research due to various applications such as prevention of crime, electronic toll system, intelligent traffic control system etc. In the proposed system, after converting the color input image into grayscale, an adaptive thresholding is used to obtain the binary image. Then the unwanted lines are removed through an unwanted-line elimination algorithm (ULEA). Finally to detect the license plate, vertical edges are detected by Sobel operator. Experiments were carried out for detection of front view as well as rear view of license plates. The experimental evaluation is carried out for 60 images taken from roadside and parking lots. The proposed method yields 96.9% detection accuracy.