Plate Detection System Research Articles

License Plate Detection Methods Based on OpenCV

---Since the technology was discovered with the automobile, this advancement in computer vision detection technology has slowly become part of the intelligent traffic management system. This technology can be used to divide vehicle images into different parts and outline specific regions for further identification across many systems. It is also highly applied in intelligent traffic management and video surveillance for automobiles among other applications. This paper introduces a new state-of-the-art License Plate Detection system using the CV2 Algorithm. Existing approaches have been highly prone to substantial sensitivity of the changes in illumination, not well defined and rather complex backgrounds themselves together with the plates which deterd recognition. Generally, the proposed future system is expected to exhibit high accuracy improvement and the cost of recognition for the present system may offset the problems that the current system has. All these considerations are taken into account when designing the proposed system. The proposed system based on CV2 Algorithm is supposed to exhibit considerable efficiency and robustness in handling noisy data. In this context, we gave our working model the analytical results to prove that our proposed model is much more beyond the current system since it uses the Python programming language. KEYWORDS--- OpenCV2 Algorithm, OCR (Optical Character Recognition system), Automatic license plate recognition (ALPR), BINARIZATION, Gray threshold (GT), EDGE DETECTION, NUMBER PLATE LOCALIZATION, DESKEWING, SPYDER3.

Study On Advanced Vehicle Number Plate Detection Systems-A Review

In response to the rising volume of vehicles on roadways, there's an urgent demand for advanced technologies to bolster security and streamline traffic-related operations. This project introduces an innovative solution: an Advanced Automatic Number Plate Detection System. Leveraging Python, Easy OCR, and Open CV, this system aims to develop a sophisticated Automatic Number Plate Recognition (ANPR) mechanism capable of accurately detecting license plates, extracting alphanumeric characters, and providing actionable insights for various applications. Python is chosen for its versatility and ease of use, serving as the primary programming language. OpenCV is employed for its robust image processing tools and license plate detection capabilities, while Easy OCR simplifies character extraction from license plates. Key features include license plate detection through OpenCV algorithms, character recognition via Easy OCR, and real-time processing suitable for tasks like traffic monitoring and surveillance. The project follows a structured approach encompassing data collection, image pre-processing, machine learning model training, integration, and thorough testing. Anticipated outcomes include a fully operational ANPR system with high accuracy in license plate detection and character recognition, surpassing conventional methods. Its potential integration into real-world applications such as traffic management underscores its significance in addressing challenges posed by increasing vehicle numbers, with YOLO v8 utilized for precise outcomes. Keywords- Python, Open CV, YOLO, tensorflow, Machine learning, ANN, CNN, and Easy OCR

Vehicle Number Plate Detection Under Different Environmental Conditions

The main aim of the project is to detect the number plate of the vehicle and display the corresponding vehicle number. The motive of this project stems from the need for robust and reliable number plate detection systems that can perform effectively in real-world scenarios. Existing techniques frequently have trouble dealing with different lighting situations, like dimly lit areas, which can result in erroneous or missed detections. Furthermore, unfavourable angles of the number plates might make the detection issues worse. The problem statement of this project is to develop a number plate detection system that is capable of accurately locating number plates on vehicles, irrespective of the prevailing environmental conditions. To achieve this, we employ a multi-stage approach that combines computer vision techniques and image processing methodologies. Our proposed approach consists of three stages which includes: pre-processing, feature extraction and text extraction

Real Time Car Model and Plate Detection System by Using Deep Learning Architectures

Real Time Car Model and Plate Detection System by Using Deep Learning Architectures

A Smart Real-Time Parking Control and Monitoring System.

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle's license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.

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.

Desain Sistem Pendeteksi Plat Kendaraan Untuk Pengisian BBM Menggunakan Metode K-Nearest Neighbor (KNN)

This study aims to design and develop a vehicle plate detection system for refueling. Applications built using the K-Nearest Neighbor (KNN) method, this method is used to classify vehicle plates. This research will use the OpenCV module in the python programming language to recognize vehicle plates and carry out the pre-processing, segmentation, feature extraction, and classification stages of vehicle Image data. This system will utilize the K-Nearest Neighbor (KNN) method such as edge detection, perspective transformation, and pattern recognition to achieve optimal results. The test results show that the system is able to recognize with high accuracy various types of vehicle license plates under different lighting conditions and viewing angles, thus a system can be created and determined for each license plate to only be refueled once a day. When the vehicle has never been filled on that day, the system will display a green light which means refueling can be done. Whereas for vehicles that have filled in on that day, the system will display a red light which means the vehicle cannot fill up so it must wait the next day after the system is reset. This system will be able to accurately and real-time detect and identify the vehicle plates used in the refueling process. Therefore, the design of this system is expected to be the basis for increasing efficiency in the refueling industry as well as providing a basis for the development of further research in this field.

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

Image Enhancement and Automated Number Plate Recognition

Automatic Number Plate Recognition (ANPR) and Vehicle Number plate detection (VNPD) systems. ANPR, is a technology that enables automatic detection, recognition, and identification of vehicle license plates, while VNPD is a subset of ANPR that focuses specifically on detecting and recognizing license plates.Several researchers have explored different approaches to ANPR and VNPD systems, as evidenced by the various papers listed. For instance, presented an e security system for vehicles number tracking at a parking lot, proposed a method of monitoring traffic signals, and violations using ANPR and GSM. In order to proceed, the conventional Grab Cut algorithm must first interactively give a candidate frame. for the target detection job to be done.To automate the identification of the licence plate by the Grab Cut algorithm, we update the candidate frame by incorporating the aspect ratio of the licence plate as the foreground extraction feature. Then, to fully implement picture noise reduction, we combined the Bernsen algorithm with the Wiener filter, which is extensively used in the fields of digital signal processing in order to increase the detection precision of conventional target identification techniques. Overall, the papers listed in the question demonstrate the wide-ranging applications of ANPR and VNPD technologies, from parking lot security to traffic signal control to driver assistance systems. These technologies have the potential to improve safety, efficiency, and security on the road, and researchers continue to explore new approaches to their development and implementation. Keywords: Detection of Number Plate, Convolutional Neural Network-(CNN), Object detection, character identification, Machine Learning-(ML).

Automatic Vehicle Number Plate Detection System

Automatic Vehicle number plate detection system is a image processing prototype, which will first process on image, then identify the number plate to get information about the vehicle. The main objective is to correctly design an automatic car identity machine with the aid of the usage of the vehicle's number plate. If the vehicle is an un-authenticate, then it becomes a very tedious and time-consuming and very hard task to search that vehicle. Recognized number plate displays on graphical user interface and stored in a database. It can be also used in on the entrance for security manage of a e.g. shopping malls, college campus and so on. The evolved machine initially detects the car then captures the car picture. vehicle range plate place is captured the use of the photo segmentation in a photograph. CNN is used to enhance the plate detection The ensuing records is then can be used to evaluate with the information on a database to give you the unique records like the car owner, region of registration, and so forth. The machine is carried out on Python(Programming Language) and OpenCV as an image processing library, and its performance is tested on real photographs. It's has been determined from the experiment that the evolved device readily acknowledges and detects the automobile's quantity plate on real images. Key Words: ANPR, OCR, CNN, OpenCV, Flask

Substation Danger Sign Detection and Recognition using Convolutional Neural Networks

This paper focuses on the training of a deep neural network regarding danger sign detection and recognition in a substation. It involved applying the concepts of neural networks and computer vision to achieve results similar to traffic sign and number plate detection systems. The input data were captured in three distinct formats, i.e. grayscale, RGB, and YCbCr, which have been used as a base for comparison in this paper. The efficiency of the neural network was tested on a unique data set involving danger signs present in industrial and processing facilities. The data set was unique, consisting of four distinct symbols. The trained data were selected so that they would not facilitate overfitting and also would not be under fitted. The accuracy of the model varied with the input type and was tested with two distinct classifiers, CNN and SVM, and the results were compared. The model was designed to be fast and accurate, and it can be implemented on mobile devices.

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.

An Improved Visual Attention Model for Automated Vehicle License Plate Number Recognition Using Computer Vision

The role of an automatic licensed plate detection system (ALPDS) cannot be over-emphasized in the world today. The need for an automated system for vehicle license plate number recognition is important for security challenges. Therefore, this paper provides a smart system for vehicle license number recognition using Computer Vision. The system was trained using images of vehicles license numbers as training data. The training images were first annotated using the Visual Graphic Generator (VGG) annotation tool, after the annotation process, the trained images were pre-processed using the OpenCV library for conversion and masking of images. TesseractOCR was then used in extracting just texts from the images. The pre-processed and segmented images were then used in training the Mask R-CNN from a pre-trained weight. The result of the proposed system shows how the Mask R-CNN model was trained in ten training steps. The mask R-CNN model obtained accuracy and a loss value for each training step. The mask R-CNN model was evaluated using both training and test data. For the training and testing data, the Mask R-CNN was evaluated in terms of accuracy and loss. The evaluation was done using graphs. The results from the graph show that the Mask R-CNN had a better accuracy result in both training and testing data. The accuracy for training data was that of 95.25% and the accuracy for the testing data was 97.69%. For real-time vehicle license plate number recognition, we deployed our proposed model to the web. Here, we built a web application that allows real-time surveillance video. Our model was tested on different vehicles in the car park. The result of the mask R-CNN on the test shows how the Mask R-CNN model was used in not just capturing and extracting the vehicle’s license plate number but predicting the characters that appeared on the vehicle’s license plate number. We also compared our proposed system with another existing system. The comparison was done in terms of accuracy, loss, and precision. The result of our proposed model gave us an accuracy of 97.69%, which is higher than the existing system (85%). This study can further be improved by using the Internet of Things in performing live video streaming and also providing a database system that will be storing the predicted vehicle numbers for cars that are detected.

An Enhanced Approach for Number Plate Detection and Recognition

Introduction: The number of vehicles is increasing day by day in our life. The vehicle may violate traffic rules and cause accidents. The automatic number plate detection system (ANPR) plays a significant role to identify these vehicles. Number plate detection is very difficult sometimes because each country has its own format for representing the number plate and font types and sizes may also vary for different vehicles. The number of ANPR systems is available nowadays but still, it is a big problem to detect the number plate correctly in various scenarios like high-speed vehicle, number plate language, etc. Methods: In the development of this method, we mainly used wiener filter for noise removal, morphological operations for number plate localization, connected component algorithm for character segmentation, and template based matching for character recognition. Results: Our proposed methodology is providing promising results in terms of detection accuracy. Discussion: The automatic number plate detection system (ANPR) has wide range of applications because the license number is the crucial, commonly putative and essential identifier of motor vehicles. These applications include ticketless parking fee management, parking access automation, car theft prevention, security guide assistance, Motorway Road Tolling, Border Control, Journey Time Measurement, Law Enforcement and many more. Conclusion: In this paper, an enhanced approach of automatic number plate detection system is proposed using some different techniques which not only detect the number plate of the vehicle but also recognize each character present in the detected number plate image.

IMPLEMENTASI ALGORITMA YOLO DAN TESSERACT OCR PADA SISTEM DETEKSI PLAT NOMOR OTOMATIS

Currently, vehicle number plate detection systems in general still use the manual method. This will take a lot of time and human effort. Thus, an automatic vehicle number plate detection system is needed because the number of vehicles that continues to increase will burden human labor. In addition, the methods used for vehicle number plate detection still have low accuracy because they depend on the characteristics of the object being used. This study develops a YOLO-based automatic vehicle number plate detection system. The dataset used is a pretrained YOLOv3 model of 700 data. Then proceed with the number plate text extraction process using the Tesseract Optical Character Recognition (OCR) library and the results obtained will be stored in the database. This system is web-based and API so that it can be used online and on the cross-platform. The test results show that the automatic number plate detection system reaches 100% accuracy with sufficient lighting and a threshold of 0.5 and for the results using the Tesseract library, the detection results are 92.32% where the system is successful in recognizing all characters on the license plates of cars and motorcycles. in the form of Alphanumeric characters of 7-8 characters.