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License plate recognition (LPR) is a crucial task in traffic management, but traditional methods face limitations in accuracy and speed that are mutually constraining. In this paper, we propose an efficient license plate recognition system that achieves high recognition accuracy while ensuring real-time recognition. In order to achieve high detection accuracy while minimizing computational effort in the license plate detection stage, we propose a lightweight cross stage partial dilated convolution (CSPDC) network. Firstly, we propose a lightweight downsampling design that reduces the computational effort of downsampling while retaining important feature information. Secondly, we introduce a lightweight feature extraction network that reduces computational effort and parameter count while maintaining the network's feature extraction capability. Finally, to prevent a decrease in detection performance after lightweight processing, we propose a cross stage partial dilated block that expands the receptive field of feature extraction to enhance the network's learning capability. Experimental results on the CCPD dataset demonstrate that our proposed system achieves a tradeoff between computational effort and accuracy, with an ACC of 99.3% and a detection speed of 89 FPS. We further deployed and tested our algorithm on the Huawei M6 tablet, and the test results shows the practical value of our proposed method. [ABSTRACT FROM AUTHOR]

The vehicle license plate detection plays a key role in Intelligent Transportation Systems. Detecting license plates, such as cars, trucks, and vans, is useful for law enforcement, surveillance, and toll booth operations. How to detect license plates quickly and accurately is crucial for license plate recognition. However, the uneven light condition or the oblique shooting angle of the license plate to be detected changes dramatically in real-world complex capture scenarios and the detection difficulty increases. At the same time, distance, lighting, angle, and other requirements are quite high, which seriously affects the detection performance. Therefore, an improved YOLOv7 integrating the parameter-free attention module SimAM for license plate detection was proposed, namely YOLO-SLD. Without modifying the original ELAN architecture, which is the key component of YOLOv7, a SimAM mechanism was added at the end of the ELAN to better extract license plate features and increase computational efficiency. More importantly, the SimAM module does not require any parameters to be added to the original YOLOv7 network, reducing model computation, and simplifying the calculation process. The performance of the detection model with different attention mechanisms was tested on the CCPD dataset for the first time and the proposed method was proven to be effective. The experimental result shows that the YOLO-SLD model has higher detection accuracy and is more lightweight with mAP at 0.5 with the overall improvement in accuracy from 98.44% of the original YOLOv7 model to 98.91%, an increase of 0.47% in accuracy. The accuracy of the CCPD test subset in dark and light images has improved from 93.5% to 96.7%, an increase of 3.2% in accuracy. The parameter size of the model is reduced by 1.2 million parameters compared to the original YOLOv7 model. Its performance is better than the other prevalent license plate detection algorithms.

In view of the problems of traditional Chinese license plate recognition methods, such as the requirement of scenes, poor real-time performance, and inability to deploy on edge devices, this study proposes a Chinese license plate detection and recognition method based on YOLO(You Only Look Once) in unconstrained scenes. This method is divided into two modules: license plate detection and license plate character recognition. In the license plate detection part, the improved YOLOv5 model is used to predict four groups of key points for license plate correction based on the prediction of target candidate regions, and the pre-training model trained on the COCO data set is used for training, which reduces the error detection problem caused by the complex environment and has high real-time performance. In the license plate character recognition part, the CRNN(Convolutional Recurrent Neural Network) model is improved, which greatly reduces the parameters and computation of the algorithm, so that it can be successfully deployed in various edge devices. Experimental results show that the proposed method can efficiently detect and recognize license plates in complex environments. The map value of the proposed license plate detection model is 3.0% higher than that of Retina-face in the license plate detection data set. Compared with LPR-Net, the accuracy of license plate character recognition model in license plate recognition data set is improved by 4.2%. [ABSTRACT FROM AUTHOR]

License plate detection plays a significant role in intelligent transportation systems. Convolutional neural networks have shown a remarkable performance and made significant progress for the detection task. Despite these outstanding achievements, license plate detection in complex environments is still a challenging task, due to the noisy background, unpredictable environments and varying shapes and sizes of the license plates. In order to improve the performance of license plate detection in complex environments, we propose a novel approach using an attention-guided dual feature pyramid and a cascaded positioning strategy. At first, the original features of images are extracted by the residual network. In order to make sure that each feature map contains higher- and lower-level semantic information, we utilize a bottom-up and a top-down pathway, respectively. Meanwhile, the proposed attention-guided dual feature pyramid network is used to receive the extracted features for a multilevel feature fusion. Our proposed attention-guided modules contain both spatial and channel attention. Attention-guided modules deduce the attention weights according to channel and spatial dimensions and multiply the calculated result with the input to obtain the refined feature maps. Then, a region proposal network is used to generate the candidate regions for the license plates. Finally, a cascaded positioning network is utilized to obtain the final locations of the license plates. To validate the effectiveness of the proposed method, we conducted a series of experiments on different public datasets. Experiments on AOLP and CCPD validated the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Automatic License Plate Recognition (ALPR) is one of the applications that hugely benefited from Convolutional Neural Network (CNN) processing which has become the mainstream processing method for complex data. Many ALPR research proposed new CNN model designs and post-processing methods with various levels of performances in ALPR. However, good performing models such as YOLOv3 and SSD in more general object detection and recognition tasks could be effectively transferred to the license plate detection application with a small effort in model tuning. This paper focuses on the design of experiment (DOE) of training parameters in transferring YOLOv3 model design and optimising the training specifically for license plate detection tasks. The parameters are categorised to reduce the DOE run requirements while gaining insights on the YOLOv3 parameter interactions other than seeking optimised train settings. The result shows that the DOE effectively improve the YOLOv3 model to fit the vehicle license plate detection task.

Auto License Plate Detection and Recognition (ALPDR) is required in many industrial fields, including visual surveillance systems and vehicle registration control. Even though this field has recently demonstrated high performance according to rapid advances in Deep Learning (DL) based technologies, there are still two problems that most related works have not yet solved. One is layout-dependent and the other is the problem of recognition accuracy degradation due to unconstrained environment conditions. In this paper, we present a more accurate, flexible and layout-independent method to improve LP detection and recognition accuracy under various outdoor conditions. For this, we proposed a new framework with lightweight and efficient anchor-free detection networks, employing the idea of CenterNet and attention-based recognition networks with residual deformable block suitable for LPR tasks. Various experiments demonstrate the performance of the proposed method, using famous LP benchmark datasets such as CCPD, AOLP and VBLPD. The proposed method outperformed the conventional LP detection and recognition methods. Additionally, Korean handicapped parking card (KHPC) data was tested to prove the usefulness of this method for marked character data different to license plates.

The application of license plate recognition technology is becoming more and more extensive. In view of the current practical requirements for the recognition accuracy and real-time performance of license plate recognition system in complex scenes, the existing target detection methods and license plate recognition methods are studied, and a car license plate recognition method based on improved YOLOv5m and LPRNet model is proposed. On the basis of studying the YOLOv5m algorithm and the image features of the car license plate, the YOLOv5m algorithm is improved from three aspects: the K-means++ algorithm is used to improve the matching degree between the anchor frame and the detection target, the DIOU loss function is used to improve the NMS method, and the feature map with $20\times 20$ is removed to reduce the number of detection layers. A lightweight LPRNet network is used to realize license plate character recognition without character segmentation. Combining the improved YOLOv5m algorithm with LPRNet network, a license plate recognition system based on IYOLOv5m-LPRNet model is designed. The experimental results show that the average recognition accuracy of license plates in front, tilt, night and strong light interference scenes is more than 98%; Compared with the models of YOLOv3-LPRNet, YOLOv4-LPRNet, YOLOv5s-LPRNet and YOLOv5m-LPRNet, the recognition accuracy and recall rate of this method are improved, reaching 99.49% and 98.79% respectively; The mAP of this method is also the highest, reaching 98.56%; In terms of recognition speed, this method is also faster than the other four methods, and the number of pictures processed per second is increased by 5 compared with the YOLOv5m-LPRNet model. Therefore, the improved license plate recognition method in this paper performs well in robustness and speed.

The development of the Automatic License Plate Recognition (ALPR) system has received much attention for the English license plate. However, despite being the sixth-largest population around the world, no significant progress can be tracked in the Bengali language countries or states for the ALPR system addressing their more alarming traffic management with inadequate road-safety measures. This paper reports a computationally efficient and reasonably accurate Automatic License Plate Recognition (ALPR) system for Bengali characters with a new end-to-end DNN model that we call Bengali License Plate Network (BLPnet). The cascaded architecture for detecting vehicle regions before vehicle license plate (VLP) is proposed to eliminate false positives, resulting in higher detection accuracy of VLP. Besides, a lower set of trainable parameters is considered for reducing the computational cost, making the system faster and more compatible for a real-time application. With a Convolutional Neural Network (CNN) based new Bengali OCR engine and word-mapping process, the model is characters-rotation invariant, and can readily extract, detect and output the complete license plate number of a vehicle. The model feeding with 17 frames per second (fps) of real-time video footage can detect a vehicle with the Mean Squared Error (MSE) of 0.0152, and the mean license-plate-character recognition accuracy of 95%. While compared to the other models, an improvement of 5% and 20% were recorded for the BLPnet over the prominent YOLO-based ALPR model and the Tesseract model for the number-plate detection accuracy and time requirement, respectively.

The automatic detection and recognition for motorcycle license plates present a very challenging task since they appear more compact and versatile than vehicle license plates. In this paper, we present an efficient detection and recognition system for motorcycle license plates based on decision tree and deep learning. It can be successfully carried out under various conditions, such as frontal, horizontally or vertically skewed, blurry, poor illumination, large viewing distances or angles, distortions, multiple license plates in an image, at night or interfered with brake lights, and headlights. Experimental results show that our system performs the best when testing with multiple license plates images under different conditions as compared against six state-of-the-art methods. Furthermore, our detection and recognition system have shown more accurate results than three commercial automatic license plate recognition systems in evaluation using accuracy, precision, recall, and F1 rates. [ABSTRACT FROM AUTHOR]

The license plate (LP) has regular and prominent edge contour, which we believe is a key factor to improve the precision of LP detection. Therefore, we propose a novel Edge-Guided Sparse Attention (EGSA) mechanism, which is composed of an Edge-Guided Component (EGC) and a Sparse Attention Component (SAC) in tandem. EGC is designed as an end-to-end edge-preserving smoothing filter, which makes full use of convolutional neural networks (CNNs) and edge-guided map to enhance the edge contour of LPs and suppress noise effectively. On this basis, the K most relevant features adaptively searched by SAC are aggregated, thereby obtaining useful long-range dependencies. EGSA mechanism pays attention to the distinguishable regions and the important edge characteristics of LPs at the same time, so that the model can locate more precisely in the complex scenes. By evaluating state-of-the-art attention methods and LP detection models on both CCPD and AOLP datasets, we demonstrate the high precision and strong generalization capability of our proposed method. [ABSTRACT FROM AUTHOR]

Data compression techniques allow data size to be reduced prior to data transmission and involve decompression upon transfer. This study shows for the first time that license plate (LP) detection can be accomplished without full decompression of the encoded data. Therefore, by determining in advance which images are required for LP recognition, computational costs of the system can be reduced. The proposed approach is realized on High Efficiency Video Coding (HEVC) based compressed video sequences. Two methods are provided that generate images from HEVC attributes. Fully decoded pixel domain images are also generated for comparative purposes from the same encoded data. The YOLO V3 Tiny Object Detector is used in order to detect LPs in the generated images. EnglishLP, a public dataset, is used to interpret the findings in terms of speed and precision and for comparison with previous studies. An additional contribution of the paper is that a new compressed domain LP database has been created and made publicly available, comprising images captured by a commercial license plate recognition system. Using at least two-orders-of-magnitude less amount of data, the proposed compressed domain LP detector achieved similar precision and recall values to those of the state-of-the-art LP detection schemes tested on both datasets. Moreover, the proposed method results in more than 30% saving in inference time. The results suggest that the proposed method can be utilized for rapid video archive searching applications.

With the explosive growth in the number of vehicles in use, automated license plate recognition (ALPR) systems are required for a wide range of tasks such as law enforcement, surveillance, and toll booth operations. The operational specifications of these systems are diverse due to the differences in the intended application. For instance, they may need to run on handheld devices or cloud servers, or operate in low light and adverse weather conditions. In order to meet these requirements, a variety of techniques have been developed for license plate recognition. Even though there has been a notable improvement in the current ALPR methods, there is a requirement to be filled in ALPR techniques for a complex environment. Thus, many approaches are sensitive to the changes in illumination and operate mostly in daylight. This study explores the methods and techniques used in ALPR in recent literature. We present a critical and constructive analysis of related studies in the field of ALPR and identify the open challenge faced by researchers and developers. Further, we provide future research directions and recommendations to optimize the current solutions to work under extreme conditions.

Automatic license plate recognition (ALPR) is generally considered a solved problem in the computer vision community. However, most of the current works on ALPR are designed to work on license plates (LP) from specific countries and use country-specific information which limits their practical applicability. Such ALPR systems require changes in the algorithm to work on other countries’ LPs. Previous works on multinational LP recognition are tested on datasets from various countries that share the same LP layout. To address this issue, this study presents a deep ALPR system designed to be applicable to multinational LPs. The proposed approach consists of three main steps– LP detection, unified character recognition, and multinational LP layout detection. The system is mainly based on the you only look once (YOLO) networks. Particularly, tiny YOLOv3 was used for the first step whereas the second step uses YOLOv3-SPP– a version of YOLOv3 that consists of the spatial pyramid pooling (SPP) block. The localized LP is fed into YOLOv3-SPP for character recognition. The character recognition network returns the bounding boxes of the predicted characters and does not provide information about the sequence of the LP number. A LP number with an incorrect sequence is considered wrong. Thus, to extract the correct sequence, we propose a layout detection algorithm that can extract the correct sequence of LP numbers from multinational LPs. We collected our own Korean car plate (KarPlate) dataset and made it publicly available. The proposed system was evaluated on LP datasets from five countries which include South Korea, Taiwan, Greece, USA, and Croatia. In addition, a small dataset containing LPs from 17 countries was collected to evaluate the effectiveness of the multinational LP layout detection algorithm. The proposed ALPR system consumes about 42 ms per image on average for extracting LP number. Experimental results demonstrate the effectiveness of our ALPR system.

The process of detecting vehicles’ license plates, along with recognizing the characters inside them, has always been a challenging issue due to various conditions. These conditions include different weather and illumination, inevitable data acquisition noises, and some other challenging scenarios like the demand for real-time performance in state-of-the-art Intelligent Transportation Systems (ITS) applications. This paper proposes a method for vehicle License Plates Detection (LPD) and Character Recognition (CR) as a unified application that presents significant accuracy and real-time performance. The mentioned system is designed for Iranian vehicle license plates, which have the characteristics of different resolution and layouts, scarce digits/characters, various background colors, and different font sizes. In this regard, the system uses a separate fine-tuned You Only Look Once (YOLO) version 3 platform for each of the mentioned phases and extracts Persian characters from input images in two automatic steps. For training and testing stages, a wide range of vehicle images in different challenging and straightforward conditions have been collected from practical systems installed as surveillance applications. Experimental results show an end-to-end accuracy of 95.05% on 5719 images. The test data included both color and grayscale images containing the vehicles with different distances and shooting angles with various brightness and resolution. Additionally, the system can perform the LPD and CR tasks in an average of 119.73 milliseconds for real life data, which illustrates a real-time performance for the system and usable applicability. The system is fully automated, and no pre-processing, calibration or configuration procedures are needed.

License plate detection and recognition are still important and challenging tasks in natural scenes. At present, most methods have favorable effect on license plate recognition under restrictive conditions, and most of such license plates are shot under good angle and light conditions. However, for license plates under non-restrictive conditions, such as dark, bright, rotated conditions etc. from the Chinese City Parking Dataset (CCPD), the performance of some methods of license plate recognition will be significantly reduced. In order to improve the accuracy of license plate recognition under unrestricted conditions, a robust license plate recognition model is proposed in this paper, which mainly includes license plate feature extraction, license plate character localization, and feature extraction of characters. First of all, the model can activate the regional features of characters and fully extract the character features of license plates. Then locate each license plate character through Bi-LSTM combined with the context location information of license plates. Finally, 1D-Attention is adopted to enhance useful character features after Bi-LSTM positioning, and reduce useless character features to realize effective acquisition of character features of license plates. A large number of experimental results demonstrate that the proposed algorithm has good performance under unrestricted conditions, which proves the effectiveness and robustness of the model. In CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Tilt, CCPD-Weather, CCPD-Challenge and other sub-datasets, the recognition rates reach 99.3%, 98.5%, 98.6%, 96.4%, 99.3% and 86.6% respectively.

Automatic license plate recognition has a wide range of applications in intelligent transportation systems and is of great significance. However, most of the current work on license plate recognition focuses on the images on the front of license plates. license plate recognition in natural scenes and arbitrary perspective is still a huge challenge. To solve this problem, this work mainly studies the detection and recognition of inclined Chinese license plates in natural scenes. We propose a robust method that can detect and correct multiple license plates with severe distortion or skewing in one image and input them into the license plate recognition module to obtain the final result. Different from the existing methods of license plate detection and recognition, our method performs affine transformation during license plate detection to rectify the distorted license plate image. It can not only avoid the accumulation of intermediate errors but also improve the accuracy of recognition. As an additional contribution, we put forward a challenging Chinese license plate recognition data set, including images obtained from different scenes under a variety of weather conditions. Through a large number of comparative experiments, we have proved the effectiveness of our proposed method.

In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images. [ABSTRACT FROM AUTHOR]

Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively. [ABSTRACT FROM AUTHOR]

With the continuous development of waterway trade, intelligent waterway supervision is gradually becoming an important way to maintain waterway safety. In this field, ship license plate (SLP) detection is importance for intelligent waterway supervision. However, in practical surveillance scenarios, detecting ship license plates (SLPs) is challenging because of small size, and text areas in complex backgrounds can also be mistakenly identified as SLPs. To solve these problems, we propose a Target Occlusion Contrast Network (TOCNet) for SLP detection. Firstly, to mitigate the effects of complex backgrounds, a novel Target Occlusion Contrast Learning (TOCL) approach is proposed, which is used to widen the decision boundary of the model and reduce false alarms. Secondly, to solve the problem that small targets are difficult to be detected, we design a Similarity Fusion Module (SFM), which effectively improves the detection performance of the model for small targets by similarity weight. Finally, to further improve the localization accuracy, we propose the task-oriented decoupled head (TDH), which facilitates the localization accuracy through selective fusion. Experimental results on both the SLPD dataset and the UFPR-ALPR dataset show that our proposed modules effectively improve the detection performance of the model, and the proposed TOCNet model exhibits a better detection performance than the other existing state-of-the-art (SOTA) models. Our code and dataset are publicly available at https://github.com/ssyuyi/tocnet. • The proposed contrast learning reduces the false alarm rate of the detection model. • The proposed similarity fusion module facilitates the detection for small targets. • A task-oriented decoupling head is proposed to facilitate detection accuracy. • A dataset that can be used for ship license plate detection is provided. • The proposed TOCNet model exhibits the performance of SOTA. [ABSTRACT FROM AUTHOR]

Currently, the conventional license plate location method fails to detect the license plate under complex road environments such as severe weather conditions and viewpoint changes. Besides, it is difficult for license plate location method based on machine learning to precisely locate the area of license plate. Moreover, license plate location method may incorrectly detect similar objects such as billboards and road signs as license plates. To alleviate these problems, this article proposes a new approach to vehicle license plate location based on new model YOLO‐L and plate pre‐identification. The new model improves in two aspects to precisely locate the area of license plate. First, it uses k‐means++ clustering algorithm to select the best number and size of plate candidate boxes. Second, it modifies the structure and depth of YOLOv2 model. Plate pre‐identification algorithm can effectively distinguish license plates from similar objects. The experimental results show that authors’ proposed method not only achieves a precision of 98.86% and a recall of 98.86%, which outperforms the existing methods, but also has high efficiency in real time.

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.

Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.

In smart surveillance and urban mobility applications, camera-equipped embedded platforms with deep learning technology have demonstrated applicability and effectiveness in identifying various targets. These use cases can be found in a variety of contexts and locations. It is critical to collect relevant data from the location where the application will be deployed. In this paper, we propose an integrated vehicle type and license plate recognition system using YOLOv4, which consists of vehicle type detection, license plate detection, and license plate character detection to better support the context of Korean vehicles in multilane highway and urban environments. Using our dataset of one to four multilane images, our system detected six vehicle classes and license plates with mAP of 98.0%, 94.0%, 97.1%, and 84.6%, respectively. On our dataset and a publicly available open dataset, our system demonstrated mAP of 99.3% and 99.4% for the detected license plates, respectively. From 4K high-resolution images, our system was able to detect minuscule license plates as small as 100 pixels wide. We believe that our system can be used in densely populated regions to address the high demands for enhanced visual sensitivity in smart cities and Internet-of-Things.

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.

This paper presents a robust method to locate a license plate in a complex scene. In contrast to the most existing method which use the non-handcrafted feature to locate the license plate area, our method uses a modified handcrafted feature. We used Maximally Stable Extremal Region (MSER) combining with dominant vertical Sobel to construct essential biner images. Closing morphology operation is implemented to merge the contour extracted by MSER and dominant vertical edge detection. Based on the area and ratio of contour, license plate candidate area is selected. Furthermore, Support Vector Machine (SVM) is introduced to choose a license plate area by analyzing the Histogram of Oriented Gradient (HOG) of each candidate. For performance evaluation, two datasets consisting of complex scene images under different conditions are tested. The main advantage of this approach is that it is faster than non-handcrafted feature-based method while maintaining the high accuracy of plate detection. [ABSTRACT FROM AUTHOR]

Number Plate recognition, also called License Plate realization or recognition using image processing methods is a potential research area in smart cities and Internet of Things. Many of the existing automated number plate recognition systems work only in a controlled environment where images are captured from a straight angle with good illumination, clarity and standard fonts. Another drawback of existing works is that, they are based on UK number plates and may not suite for Indian number plates. This paper presents a novel image processing system for Indian number plate detection and recognition that can deal with, noisy, low illuminated, cross angled, non-standard font number plates. This work employs several image processing techniques such as, morphological transformation, Gaussian smoothing, and Gaussian thresholding in the pre-processing stage. Next, for number plate segmentation, contours are applied by border following and contours are filtered based on character dimensions and spatial localization. Finally, after the region of interest filtering and de-skewing, K-nearest neighbor algorithm is used for character recognition. The proposed methods demonstrated promising results. [ABSTRACT FROM AUTHOR]

In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images.

• License plate detection for multi-national vehicles in a multi-lane scenario. • Ability to deal with multi-national license plates of various colors and styles. • Capability to handle illumination variance. • Performance evaluation on datasets of China, Thailand, Pakistan, USA, UAE and Netherlands. • Superior performance as compared to conventional and deep learning approaches. Only a few methods in literature are effective for multi-national license plate detection in a multi-lane scenario. These methods are prone to illumination variance, complex background and weak-edged license plates. In this paper, we propose a novel illumination invariant method to handle multi-national vehicle license plates of different colors and styles. Red corona is initially used to detect the tail-lights of vehicles to establish region-of-interest as the license plates are in a vicinity of its tail-lights. The vertical edges within each region-of-interest are obtained using a unique approach that preserve license plate edges for improved performance. Heuristic energy map is then used to distinguish the license plate area. To validate the detected regions, high-level features extracted from AlexNet Convolutional Neural Network are used. Extensive experiments on the license plates of six countries show that the proposed approach not only ensures real-time performance, but also outperforms the conventional and deep-learning methods. [ABSTRACT FROM AUTHOR]

Currently, the conventional license plate location method fails to detect the license plate under complex road environments such as severe weather conditions and viewpoint changes. Besides, it is difficult for license plate location method based on machine learning to precisely locate the area of license plate. Moreover, license plate location method may incorrectly detect similar objects such as billboards and road signs as license plates. To alleviate these problems, this article proposes a new approach to vehicle license plate location based on new model YOLO‐L and plate pre‐identification. The new model improves in two aspects to precisely locate the area of license plate. First, it uses k‐means++ clustering algorithm to select the best number and size of plate candidate boxes. Second, it modifies the structure and depth of YOLOv2 model. Plate pre‐identification algorithm can effectively distinguish license plates from similar objects. The experimental results show that authors' proposed method not only achieves a precision of 98.86% and a recall of 98.86%, which outperforms the existing methods, but also has high efficiency in real time. [ABSTRACT FROM AUTHOR]

As the license plate is multiscale and multidirectional in the natural scene image, its detection is challenging in many applications. In this work, a novel network that combines indirect and direct branches is proposed for license plate detection in the wild. The indirect detection branch performs small-sized vehicle plate detection with high precision in a coarse-to-fine scheme using vehicle–plate relationships. The direct detection branch detects the license plate directly in the input image, reducing false negatives in the indirect detection branch due to the miss of vehicles’ detection. We propose a universal multidirectional license plate refinement method by localizing the four corners of the license plate. Finally, we construct an end-to-end trainable network for license plate detection by combining these two branches via post-processing operations. The network can effectively detect the small-sized license plate and localize the multidirectional license plate in real applications. To our knowledge, the proposed method is the first one that combines indirect and direct methods into an end-to-end network for license plate detection. Extensive experiments verify that our method outperforms the indirect methods and direct methods significantly.

Traffic surveillance is an important issue in intelligent transportation systems. Efficient and accurate vehicle detection is one challenging problem for complex urban traffic surveillance. As such, this paper proposes a new vehicle detection method using spatial relationship GMM for daytime and nighttime based on a high-resolution camera. First, the vehicle is treated as an object composed of multiple components, including the license plate, rear lamps and headlights. These components are localized using their distinctive color, texture, and region feature. Deriving plate color converting model, plate hypothesis score calculation and cascade plate refining were accomplished for license plate localization. Multi-threshold segmentation and connected component analysis are accomplished for rear lamps localization. Frame difference and geometric features similarity analysis are accomplished for headlights localization. After that, the detected components are taken to construct the spatial relationship using GMM. Finally, similar probability measures of the model and the GMM, including GMM of plate and rear lamp, GMM of both rear lamps and GMM of both headlights are adopted to localize vehicle. Experiments in practical urban scenarios are carried out under daytime and nighttime. It can be shown that our method can adapt to the partial occlusion and various lighting conditions well, meanwhile it has a fast detection speed. [ABSTRACT FROM AUTHOR]

We have developed a license plate identification method for Myanmar vehicles that are captured under dissimilar conditions, e.g., angle of image capturing, different types of license plates, and real environmental conditions. In this study, car license plate recognition (CLPR), bounding box, horizontal and vertical dilations, skew angle detection, and plate detection were proposed to identify license numbers from different vehicle images. To recognize the characters, a new algorithm based on deep learning, a subset of artificial intelligence (AI), is proposed. The neural nets are progressing rapidly in many fields. The applied model of neural network is used for classification. The recognition part is a very challenging task. Compared with the traditional method, the neural network has obvious advantages. The benefit of this research is to eliminate the need of license plate recognition (LPR) under different conditions. In mobile phones, there are many sensors used to detect the presence of nearby objects. Accelerometers in mobile phones are used. Developed for the Samsung mobile phone, sensors can yield sensor readings but it not much else. Each car was viewed from four different angles under different conditions. In our experiment, the results showed an average accuracy of 97%, which was substantially applied to license plate identification under different environmental conditions. To extend the experiment, the vehicle images were also collected under different conditions, such as dark and cloudy weather and various sizes and positions of plates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

License plate detection (LPD) is the first and key step in license plate recognition.State-of-the-art object-detection algorithms based on deep learning provide a promising form ofLPD. However, there still exist two main challenges. First, existing methods often enclose objectswith horizontal rectangles. However, horizontal rectangles are not always suitable since licenseplates in images are multi-oriented, reflected by rotation and perspective distortion. Second, thescale of license plates often varies, leading to the difficulty of multi-scale detection. To addressthe aforementioned problems, we propose a novel method of multi-oriented and scale-invariantlicense plate detection (MOSI-LPD) based on convolutional neural networks. Our MOSI-LPD tightlyencloses the multi-oriented license plates with bounding parallelograms, regardless of the licenseplate scales. To obtain bounding parallelograms, we first parameterize the edge points of licenseplates by relative positions. Next, we design mapping functions between oriented regions andhorizontal proposals. Then, we enforce the symmetry constraints in the loss function and train themodel with a multi-task loss. Finally, we map region proposals to three edge points of a nearby licenseplate, and infer the fourth point to form bounding parallelograms. To achieve scale invariance, wefirst design anchor boxes based on inherent shapes of license plates. Next, we search different layersto generate region proposals with multiple scales. Finally, we up-sample the last layer and combineproposal features extracted from different layers to recognize true license plates. Experimental resultshave demonstrated that the proposed method outperforms existing approaches in terms of detectinglicense plates with different orientations and multiple scales.

Abstract 1 1 HSI: hue-saturation-intensity; 2D: two-dimensional; 3D: three-dimensional; REG: special register. This paper presents a new algorithm for detecting and extracting license plates from a complex image by using a fuzzy two-dimensional Gabor filter. The filter parameters are fuzzified to optimize the Gabor filter. In particular, the orientation and wavelengths of the Gabor filter are fuzzified. Fuzzification of the wavelength results in greater selectivity. These parameters dominate the filtering results. Bell’s function and triangular membership function were proven to be the most efficient method for selecting the filter parameters in our experiments and were used for fuzzification. The use of these filters provided satisfactory results; the components of interest were efficiently extracted, and the procedure was found to be very noise-resistant. [ABSTRACT FROM AUTHOR]

An algorithm that projects image intensity along a radial line oriented at a specific angle enables the recognition of vehicular license plates at odd angles. The algorithm determines orientation angle, rotates the image to a horizontal perspective, and removes image noise from rotation to achieve recognition accuracy as high as 98 percent. [ABSTRACT FROM AUTHOR]

License Plate Recognition (LPR) system plays a very important role in intelligent traffic control and security management, such as parking enforcement, stolen vehicle recovery and surveillance operations. This research describes a new method for the number recognition of Tunisian license plate. Based on low pass filter in license plate extraction module and network in LPR, the proposed method shows a great robustness in most unfavorable conditions. [ABSTRACT FROM AUTHOR]