License Plate Recognition Method Research Articles

A method and software for license plate recognition

В статті представлений метод розпізнавання номерних знаків із використанням сегментації шляхом використання системи детектування YOLO у поєднанні із завдання-орієнтованим підходом до процесу навчання та використанням масивів варіативних даних реального світу. Розвиток мегаполісів і постійне збільшення кількості транспортних засобів на дорогах призвели до нового рівня вимог до систем безпеки дорожнього руху. Автоматизація, без перебільшення, є найбільш пріоритетним напрямком розвитку цих систем. Лише за допомогою автоматизації системи безпеки дорожнього руху можуть обробляти величезну кількість інформації, що генерується на дорогах щодня. Крім того, автоматизація дозволяє поступово зменшувати участь людини в задачах, які обчислювальні системи можуть виконувати з еквівалентною або більшою точністю. Ці досягнення спрямовані на мінімізацію впливу людського фактору, а також на зниження експлуатаційних витрат. Це особливо важливо для мегаполісів, але також стосується транспортної системи в цілому. Метою дослідження є розробка методу автоматизованого розпізнавання номерних знаків для підвищення точності систем забезпечення дорожньої безпеки шляхом зниження рівня помилок, мінімізації надмірного використання обчислювальних ресурсів у процесі виявлення та здешевлення таких систем. Об’єктом дослідження є процес розробки автоматизованих програмних систем для забезпечення дорожньої безпеки з інтеграцією функціоналу ідентифікації транспортних засобів. Для досягнення поставленої мети були визначені такі завдання: розробити метод розпізнавання номерних знаків із застосуванням цілеспрямованого підходу до навчання у поєднанні з системою виявлення YOLO; оцінити вплив попередньої сегментації номерних знаків із використанням спеціально навченої системи YOLO на рівень помилок і часові витрати, а також провести експерименти із застосуванням запропонованого методу навчання на реальних зображеннях із варіативним довкіллям для підтвердження його адекватності. Порівняльний аналіз використання завдання-орієнтованого методу навчання системи детектування на базі YOLO v5 лише з загальноприйнятим методом оптичного розпізнавання символів (Optical Character Recognition, OCR) підтвердив переваги завданняорієнтованого методу при вирішенні завдання з розпізнавання номерних знаків. Також було досліджено вплив розмиття на результати детектування із використанням OCR методу. Результати практичних досліджень підтверджують правильність обраних методів для підвищення ефективності розпізнавання номерних знаків.

License Plate Recognition Using Three-Dimensional Rotated Character Recognition and Instance Segmentation by Deep Learning

License plate recognition is currently used in various situations, such as parking lot vehicle management and the tracking of wanted vehicles. These applications require recognition from all possible camera angles. Therefore, we propose a method for license plate recognition in images captured from various camera angles. First, the license plate area is detected from the input image using the YOLOv5 object detection method. The subsequent process can be performed in two manners: by rotating the license plate to the front using projective transformation and trapezoidal correction, or by using graph matching. The first method performs Hough transforms or linear approximations as preprocessing, and then calculates the rectangle surrounding the license plate from the YOLOv5 output. After performing trapezoidal correction, the maximally stable extremal regions (MSER) are used to detect character candidates, and characters are recognized using 3D rotated character recognition. The second method performs character candidate detection and recognition without trapezoidal correction, followed by graph matching. These methods are versatile because they do not depend on the layout of license plates, which varies among countries and regions. Two types of datasets were used: a set of images containing Japanese license plates collected by ourselves, and a set of publicly available images containing Taiwanese license plates. Two recognition rates were evaluated: the license plate character recognition rate and license plate recognition rate in which all characters in one plate are recognized. The license plate character recognition rates using graph matching were 91.9% for the Japanese license plates and 89.3% for the Taiwanese license plates. The license plate recognition rates were 84.0% and 84.5%, respectively.

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

Unleashing the power of Convolutional Neural Networks in license plate recognition and beyond

This article explores the application of Convolutional Neural Networks (CNN) in the field of license plate recognition. It begins by introducing the architecture of CNN, which consists of three key layers: Convolutional Layers, Pooling Layers, and Fully Connected Layers. The article then references three relevant papers that demonstrate how CNNs are applied in license plate recognition. The first paper utilizes TensorFlow to construct a CNN model and integrates it with an STM32MP157 embedded chip for license plate recognition. The second paper presents a real-time car license plate detection and recognition method called Multi-Task Light CNN, emphasizing robustness. The third paper employs the ResNet+FPN feature extraction network of the Mask R-CNN model and annotates a license plate dataset. The article highlights the promising future of CNNs in various fields beyond license plate recognition, emphasizing their potential for further development and industrial applications. CNNs have proven to be versatile and powerful tools in computer vision, offering solutions to a wide range of problems. Their adaptability and effectiveness make them a key player in the ongoing advancement of artificial intelligence and automation technologies.

A License Plate Detection and Recognition Method Based on Improved Yolov5

This study proposes a license plate detection and recognition method based on yolov5. In order to accurately detect the license plate and accurately identify the license plate number, this study adds key point detection based on the yolov5 algorithm to correct the license plate image into a rectangle through perspective transformation, which greatly improves the accuracy of license plate number recognition. Due to embedded deployment, this study simplifies the yolov5 network structure to a certain extent. While reducing the network depth and width, ShuffleNet Block is used to replace the C3 module in yolov5. In the end, this study achieved a mean average precision (mAP) of 89.7% while controlling the network calculation amount to 5GFLOPs. Therefore, this model has broad application prospects in the transportation field.

Method of vehicle license plate recognition in haze weather based on deep learning

Method of vehicle license plate recognition in haze weather based on deep learning

SMART PARKING SYSTEM FOR LICENSE PLATE RECOGNITION BASED ON YOLO NEURAL NETWORK AND OPTICAL CHARACTER RECOGNITION

This paper describes a license plate recognition method, exemplified by training and deploying a machine learning model. The study uses the YOLO (―You Only Look Once‖) neural network architecture and optical character recognition (OCR) techniques to extract license plate characters for real-time license plate recognition. Experimental tests, including model training, validation, and evaluation, demonstrate the effectiveness of these methods in enhancing automated access control in smart parking systems.

AFA-Net: Adaptive Feature Attention Network in image deblurring and super-resolution for improving license plate recognition

AFA-Net: Adaptive Feature Attention Network in image deblurring and super-resolution for improving license plate recognition

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.

Artificial intelligence technology in license plate recognition

Due to the importance of license plate recognition in many parts in human society, several methods are concluded and proposed in this article. In this work, artificial intelligence (AI) technology, deep learning, and neural networks are put into use in different aspects or modules of license plate recognition methods. Current methods can be divided into two categories, one is the traditional methods and the other is method using AI technology. Two-stage method is a typical example for traditional methods. In this work, other methods such as RT-LPDRnet method, which takes YOLO v5 into use, is an example for AI technology in license plate recognition. Now commercial license plate recognition generally adopts the method of end-to-end deep learning, while some methods of character segmentation first and then recognition are still worthy of study. In this work, three evaluation metrics are concluded to help evaluate the performance of previous methods. In addition, application of license plate recognition is proposed such as recognition in the parking lots, entrances and exits of expressway and electronic police system.

A CRNN‐based method for Chinese ship license plate recognition

AbstractExisting deep learning methods cannot achieve satisfactory ship license plate (SLP) recognition due to the harsh marine environment, such as foggy weather, unstable ship state and small targets. Therefore, a convolutional recurrent neural network (CRNN)‐based method is proposed for accurate SLP image recognition. Overall, the suggested method improves a CRNN recognition model by SLP image enhancement and data augmentation. The SLP image enhancement employs dark channel prior and Hough transform line detector to address the fog/blurriness and tilt issues existing in SLP images. As separate and joint operations, the two enhancements contribute to data augmentation for CRNN recognition. Preprocessing algorithms, including adaptive histogram equalization and image edge padding, are used to improve and unify the enlarged dataset for augmenting the CRNN model. As a final step, correction of the CRNN recognition results is made according to the character rule of SLPs, using an edit‐distance algorithm to match against a pre‐established SLP dictionary. A variety of real SLP images were collected to build an SLP image dataset for verification. The experimental results indicate that our method can reach an SLP recognition accuracy of , which is significantly superior to other text‐based deep learning methods.

High Precision License Plate Recognition Algorithm in Open Scene

At present, license plate recognition algorithm under restricted conditions is relatively mature and widely used in various license plate recognition system. Due to the influence of factors such as large differences in shooting angles and vehicle motion blur, Chinese license plate recognition is quite challenging. In response to the above problems, this research abandoned the single end-to-end deep learning license plate recognition method, and proposed a step-by-step license plate recognition algorithm that integrated detection and classification, and utilized a level-by-level object detection strategy combined with character classification to predict the characters of the license plate result. On the basis of the above, a multi-anchor character position regression algorithm was proposed to further accurately regress the local area position information of all license plate characters. At the same time, in order to meet the needs of character detection and character classification, as well as the imbalance of the existing license plate datasets, this study contributed a series of supporting license plate datasets. According to the published publications, this study contributed the first large-scale character-level annotated license plate dataset. Extensive experiments show that the method in this study can reach the current state-of-the-art on different datasets. If accepted, the dataset will be publicly available at https://gitee.com/wust30405/lpdataset.

A super-resolution-based license plate recognition method for remote surveillance

With the continuous development of deep learning, neural networks have made great progress in license plate recognition (LPR). Nevertheless, there is still room to improve the performance of license plate recognition for low-resolution and relatively blurry images in remote surveillance scenarios. When it is difficult to enhance the recognition algorithm, we choose super-resolution (SR) to improve the quality of license plate images and thereby provide clearer input for the subsequent recognition stage. In this paper, we propose an automatic super-resolution license plate recognition (SRLPR) network which consists of four parts separately: license plate detection, character detection, single character super-resolution, and recognition. In the training stage, firstly, LP detection model needs to be trained alone and then its detection results will be used to successively train the three subsequent modules. During the test phase, for each input image, the network can get its LP number automatically. We also collect an applicable and challenging LPR dataset called SRLP, which is collected from real remote traffic surveillance. The experimental results demonstrate that our method achieves comprehensive quality of SR images and higher recognition accuracy compared with state-of-the-art methods. The SRLP dataset and the code for training and testing SRLPR network are available at https://pan.baidu.com/s/1vnhRa-c-dBj6jlfBZV5w4g.

An Efficient and Unified Recognition Method for Multiple License Plates in Unconstrained Scenarios

License plate recognition is an important research topic in image recognition, which plays a vital role in helping the government effectively manage vehicles. At present, most license plate recognition methods focus on a single category and specific scenes. The requirements on distance, illumination, angle and other conditions are pretty high, which are not conducive to the application in practice. This paper proposes a real-time and accurate automatic license plate recognition (ALPR) method to overcome these challenges. The ALPR method contains a license plate detection network (LPDNet) and character recognition network (CRNet). LPDNet is based on the anchor-free method to detect the bounding box and four corners of license plates in an unconstrained environment. The proposed Rotating Gaussian Kernel and centrality loss enable LPDNet to improve performance in complex environments. CRNet is composed of a full convolution network, which can identify many kinds of license plates. In this network, feature extraction and compression technology have been proposed, effectively identifying multiple license plates in combination with a Character Classification module. Experiments on multiple public datasets show that the method outperforms existing methods in speed and accuracy, especially in complex and challenging environments.

Towards Automatic License Plate Recognition in Challenging Conditions

License plate recognition (LPR) is an integral part of the current intelligent systems that are developed to locate and identify various objects. Unfortunately, the LPR is a challenging task due to various factors, such as the numerous shapes and designs of the LPs, the non-following of standard LP templates, irregular outlines, angle variations, and occlusion. These factors drastically influence the LP appearance and significantly challenge the detection and recognition abilities of state-of-the-art detection and recognition algorithms. However, recent rising trends in the development of machine learning algorithms have yielded encouraging solutions. This paper presents a novel LPR method to address the aforesaid issues. The proposed method is composed of three distinct but interconnected steps. First, a vehicle that appears in an input image is detected using the Faster RCNN. Next, the LP area is located within the detected vehicle by using morphological operations. Finally, license plate recognition is accomplished using the deep learning network. Detailed simulations performed on the PKU, AOLP, and CCPD databases indicate that our developed approach produces mean license plate recognition accuracy of 99%, 96.0231%, and 98.7000% on the aforesaid databases.

License Plate Recognition Methods Employing Neural Networks

Advances in both parallel processing capabilities because of graphical processing units (GPUs) and computer vision algorithms have led to the development of deep neural networks (DNN) and their utilization in real-world applications. Starting from the LeNet-5 architecture of the 1990s, modern deep neural networks may have tens to hundreds of layers to solve complex problems such as license plate detection or recognition tasks. In this manuscript, we present a review of the state-of-the-art methods related to automatic license plate recognition. Since deep networks have demonstrated a remarkable ability to outperform other machine learning techniques, we focus only on neural network based license plate recognition methods. We highlight the particular types of networks, i.e., convolutional, residual recurrent, or long-short-term-memory, used for the specific tasks of license plate detection, extraction, or recognition in different existing works. The presented summary also highlights some of the most widely used data sets for comparison and shares the results reported in the reviewed papers. We also give an overview of the effects of fog, motion, or the use of synthetic data on license plate recognition. Finally, promising directions for future research in this domain are presented.

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.

End-to-End Light License Plate Detection and Recognition Method Based on Deep Learning

In the field of intelligent robot and automatic drive, the task of license plate detection and recognition (LPDR) are undertaken by mobile edge computing (MEC) chips instead of large graphics processing unit (GPU) servers. For this kind of small computing capacity MEC chip, a light LPDR network with good performance in accuracy and speed is urgently needed. Contemporary deep learning (DL) LP recognition methods use two-step (i.e., detection network and recognition network) or three-step (i.e., detection network, character segmentation method, and recognition network) strategies, which will result in loading two networks on the MEC chip and inserting many complex steps. To overcome this problem, this study presents an end-to-end light LPDR network. Firstly, this network adopts the light VGG16 structure to reduce the number of feature maps and adds channel attention at the third, fifth, and eighth layers. It can reduce the number of model parameters without losing the accuracy of prediction. Secondly, the prediction of the LP rotated angle is added, which can improve the matching between the bounding box and the LP. Thirdly, the LP part of the feature map is cropped by the relative position of detection module, and the region-of-interest (ROI) pooling and fusion are performed. Seven classifiers are then used to identify the LP characters through the third step’s fusion feature. At last, experiments show that the accuracy of the proposed network reaches 91.5 and that the speed reaches 63 fps. In the HiSilicon 3516DV300 and the Rockchip Rv1126 Mobile edge computing chips, the speed of the network has been tested for 15 fps.

An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification

An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization in complex real-life conditions. In this paper, we leverage a YOLO-based end-to-end generic ALPR pipeline for vehicle detection (VD), license plate (LP) detection and recognition without exploiting prior knowledge or additional steps in inference. We assess the whole ALPR pipeline, starting from vehicle detection to the LP recognition stage, including a vehicle classifier for emergency vehicles and heavy trucks. We used YOLO v2 in the initial stage of the pipeline and remaining stages are based on the state-of-the-art YOLO v4 detector with various data augmentation and generation techniques to obtain LP recognition accuracy on par with current proposed methods. To evaluate our approach, we used five public datasets from different regions, and we achieved an average recognition accuracy of 90.3% while maintaining an acceptable frames per second (FPS) on a low-end GPU.

A Hybrid Deep Learning Algorithm for the License Plate Detection and Recognition in Vehicle-to-Vehicle Communications

With the rapid development of Internet of Things (IoT) in the field of transportation, the vehicle-to-vehicle (V2V) communication not only becomes available on a large scale, but also will be an indispensable part of the future transportation. License plates are the identification of vehicles, so the license plate detection and recognition in the V2V communication scenario is very important. However, the existing license plate detection and recognition methods are suffering from a low accuracy rate issue. To solve this issue, we propose a hybrid deep learning algorithm as the license plate detection and recognition model by fusing YOLOV3 and CRNN. The proposed model enables the network itself to better utilize the different fine-grained features in the high and low layers to carry out multi-scale detection and recognition. In this model, we utilize the fast and accurate performance of YOLOV3, and the excellent detection ability of CRNN. As a result, this proposed model reaps the benefit of both. Finally, we test this proposed model in difficult scenarios and low-quality license plate images caused by weather, and results show this proposed license plate detection and recognition model can achieve a higher mean average precision, better comprehensive performance, and excellent robustness.