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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 challenge of license plate recognition (LPR) detection and recognition has different solutions in the literature. LPR systems also address various proposals for technological use, from vehicle tracking on roads to detection and prospecting of routes through plate capture. In this sense, this paper presents a new approach for character detection and recognition based on training synthetic Mercosur model plates, tested on real Mercosur model plates. The proposed study also introduces a new database (LPR-UFC) available by request, containing 1.100 real images of different vehicles with Mercosur plates. The results with the new model in conjunction with perspective adjustment obtained 14% more accuracy than the model without the plate perspective process (71.3% against 85%) for character recognition, reaching 90% accuracy for plate detection, surpassing state of the art for real dataset images (LPR-UFC). The results also beat studies found in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a real-time and accurate automatic license plate recognition (ALPR) approach. Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should abundant additional character segmentation and recurrent neural network (RNN), but adopt a plain convolutional neural network (CNN); (3) in the case of CNN, taking advantage of vertex information on license plates improves the recognition performance; and (4) the weight-sharing character classifier addresses the lack of training images in small-scale datasets. Based on these insights, we propose a novel ALPR approach, termed VSNet. Specifically, VSNet includes two CNNs, i.e., VertexNet for license plate detection and SCR-Net for license plate recognition, integrated in a resampling-based cascaded manner. In VertexNet, we propose an efficient integration block to extract the spatial features of license plates. With vertex supervisory information, we propose a vertex-estimation branch in VertexNet such that license plates can be rectified as the input images of SCR-Net. In SCR-Net, we introduce a horizontal encoding technique for left-to-right feature extraction and propose a weight-sharing classifier for character recognition. Experimental results show that the proposed VSNet outperforms state-of-the-art methods by more than 50% relative improvement on error rate, achieving >99% recognition accuracy on CCPD and AOLP datasets with 149 FPS inference speed. Moreover, our method illustrates an outstanding generalization capability when evaluated on the unseen PKUData and CLPD datasets. [ABSTRACT FROM AUTHOR]

Automatic License Plate Recognition is a crucial task for several applications related to Intelligent Transportation Systems, from access control to traffic monitoring. Most existing approaches are focused on a specific setup (e.g., toll control) or a single license plate (LP) region (e.g., European, US, Brazilian, Taiwanese, etc.), which limits their application. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. We present an Improved Warped Planar Object Detection Network (IWPOD-NET) that is able to detect the four corners of an LP in a variety of conditions, so that it can be warped to a fronto-parallel view and alleviate perspective-related distortions. Given the rectified LP, we test two different Optical Character Recognition (OCR) methods based on object detection. Our experimental results show that the proposed detector is competitive with state-of-the-art (SOTA) methods using a very limited training set. Regarding the full ALPR results, our method achieves top-scoring results for several datasets that include a variety of capture conditions and vehicle types (in particular, motorcycles). [ABSTRACT FROM AUTHOR]

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]

This paper is concerned with the detection and recognition of Chinese license plates in complex backgrounds. Most applications are currently focused on good conditions. In complex natural scenes such as CCPD-DB, CCPD-FN, CCPD-Rotate, CCPD-Tile, CCPD-Weather, and CCPD-Challenge from the Chinese City Parking Dataset (CCPD), inaccurate localization and poor character recognition accuracy issues appear towards existing license plates. Therefore, this paper proposes a two-stage license plate recognition algorithm based on YOLOv3 and Improved License Plate Recognition Net (ILPRNET). In the first stage, YOLOv3 is adopted to detect the position of the license plate and then extract the license plate. In the second stage, the ILPRNET license plate recognition network is used to perform localization of license plate characters and the 2D attentional-based license plate recognizer with an CNN encoder is capable of recognizing license plates accurately. The test results indicate that our proposed algorithm performs well in a variety of complex scenarios. Especially in sub-datasets like CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Weather, and CCPD-Challenge, the recognition accuracy achieved 99.2%, 98.1%, 98.5%, 97.8%, and 86.2%, respectively. [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.

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods. [ABSTRACT FROM AUTHOR]

In modern life, the massive number of vehicles makes it hard for a human being to process its related information. So, it is important to build an automatic system to collect information about vehicles. The license plate is the unique identifier of a vehicle. In this paper, we propose an automatic license plate recognition system. The proposed system was based on the Faster R-CNN improved by adding an adaptive attention network for the segmentation of the license plate to retrieve the numbers and the letters of identification. Also, we add a deconvolution layer at the top of the features extraction network to detect the small size of the target license plate. To train and evaluate the proposed system, a dataset was collected for Arabic countries such as Egypt, KSA, and UAE that have similar license plates with Arabic and Indian numbers, Arabic and Latin alphabets. The dataset was collected from the internet using a python script then it was filtered and annotated manually. The evaluation of the proposed model dataset results in achieving a recall of 98.65 % and a precision of 97.46 %. The developed system was able to process images in real-time with a processing speed of 23 FPS. [ABSTRACT FROM AUTHOR]

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.

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The challenge of license plate recognition (LPR) detection and recognition has different solutions in the literature. LPR systems also address various proposals for technological use, from vehicle tracking on roads to detection and prospecting of routes through plate capture. In this sense, this paper presents a new approach for character detection and recognition based on training synthetic Mercosur model plates, tested on real Mercosur model plates. The proposed study also introduces a new database (LPR-UFC) available by request, containing 1.100 real images of different vehicles with Mercosur plates. The results with the new model in conjunction with perspective adjustment obtained 14% more accuracy than the model without the plate perspective process (71.3% against 85%) for character recognition, reaching 90% accuracy for plate detection, surpassing state of the art for real dataset images (LPR-UFC). The results also beat studies found in the literature. [ABSTRACT FROM AUTHOR]

License plate detection and recognition (LPDR) has attracted considerable attention in recent years, and many algorithms have presented the competitive performance on several datasets. However, there are still three significant issues to be addressed in this field. Firstly, most methods have poor detection performance in unconstrained scenarios with moving vehicles and highly distracting background objects. Secondly, existing systems generally focus on single image-based algorithms, yet traffic video sequences provide more effective information than individual frames for LPDR tasks. Thirdly, images and videos captured in complex environments may be adversely affected by distortions and low resolution, causing sensitive recognition performance and reduced robustness. To remedy these issues, we propose to automatically perform license plate detection, tracking, and recognition in real-world traffic videos and integrate them into a unified end-to-end framework via deep learning. The contributions of this paper are threefold: 1) A deep flow-guided spatiotemporal license plate detector is proposed to model the video contextual information by introducing optical flow and a novel spatiotemporal attention mechanism; 2) An online license plate tracker is developed to bridge video-based detection and recognition which utilizes both motion and deep appearance information, and innovatively, it can be end-to-end trained with the detector via multi-task learning; 3) The efficient quality-guided license plate recommender and recognizer are proposed to jointly perform stream recognition. The former recommends high-quality frames from video streams while the latter generates recognition results. We evaluate the proposed method on three traffic video-based license plate datasets, and ablation studies have been presented to verify the effectiveness of each component mentioned above. Moreover, extensive experiments are conducted for comparison with other approaches in different scenarios, and the results have demonstrated that our method achieves state-of-the-art performance on all datasets. [ABSTRACT FROM AUTHOR]

• A new problem of license plate/text detection in drone images is addressed. • The concept of Phase congruency in DCT domain is introduced for detection. • The combination of DCT and Phase congruency is new. • Deep learning is used in different way for eliminating false positives. • The combination of feature extraction and DCT-Phase is new. License plate number detection in drone images is a complex problem because the images are generally captured at oblique angles and pose several challenges like perspective distortion, non-uniform illumination effect, degradations, blur, occlusion, loss of visibility etc. Unlike, most existing methods that focus on images captured by orthogonal direction (head-on), the proposed work focuses on drone text images. Inspired by the Phase Congruency Model (PCM), which is invariant to non-uniform illuminations, contrast variations, geometric transformation and to some extent to distortion, we explore the combination of DCT and PCM (DCT-PCM) for detecting license plate number text in drone images. Motivated by the strong discriminative power of deep learning models, the proposed method exploits fully connected neural networks for eliminating false positives to achieve better detection results. Furthermore, the proposed work constructs working model that fits for real environment. To evaluate the proposed method, we use our own dataset captured by drones and benchmark license plate datasets, namely, Medialab for experimentation. We also demonstrate the effectiveness of the proposed method on benchmark natural scene text detection datasets, namely, SVT, MSRA-TD-500, ICDAR 2017 MLT and Total-Text. [ABSTRACT FROM AUTHOR]

License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particular, small-sized and multi-oriented license plates, mainly caused by the remote and mobile camera, are challenging to detect. We propose a novel and applicable method for degraded license plate detection via vehicle-plate relation mining in this work. The proposed method can detect the license plate in a coarse-to-fine scheme. First, we propose to estimate the plate by using the relationships between the vehicle and the license plate, which can significantly reduce the search area and precisely detect small-sized license plates. Second, we present to robustly detect the multi-oriented license plate by regressing the four corners of the license plate in the local region. The whole network is constructed in an end-to-end manner, and codes are available at https://github.com/chensonglu/LPD-end-to-end. [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. [ABSTRACT FROM AUTHOR]

The management of public security protection in forest areas has always been a problem that people lack attention to. It reveals the weakness of public security protection management in forest areas that committing crimes, illegal felling of trees and poaching animals. Our group has been committed to the application of machine vision in forest areas. In order to enhance the ability of forest area to prevent illegal cutting, this paper presents a novel algorithm of license plate detection which combines image defogging processing. This method uses the technique of contrast limited adaptive histogram equalization to remove the interference of image blurring in foggy, rainy and snowy days, and combines the features of the license plate such as color, aspect ratio, etc. to detect the license plate in fog, rain and snow weather. Compared with the ordinary license plate detection method, the result of this method has been greatly improved. The accuracy of detecting license plate in fog, rain and snow weather can reach 88. 00%, while the accuracy of ordinary license plate detection can only reach 82. 67%. This paper preliminarily proves the feasibility of the proposed algorithm in fog, rain and snow weather of forest areas. [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]

License plates are the primary source of vehicle identification data used in a wide range of applications including law enforcement, electronic tolling, and access control amongst others. License plate detection (LPD) is a critical process in automatic license plate recognition (ALPR) that reduces complexity by delimiting the search space for subsequent ALPR stages. It is complicated by unfavourable factors including environmental conditions, occlusion, and license plate variation. As such, it requires training models on substantial volumes of relevant images per use case. In 2018, the new Mercosur standard came in to effect in four South American countries. Access to large volumes of actual Mercosur license plates with sufficient presentation variety is a significant challenge for training supervised models for LPD, thereby adversely impacting the efficacy of ALPR in Mercosur countries. This paper presents a novel license plate embedding methodology for generating large volumes of accurate Mercosur license plate images sufficient for training supervised LPD. We validate this methodology with a deep learning-based ALPR using a convolutional neural network trained exclusively with synthetic data and tested with real parking lot and traffic camera images. Experiment results achieve detection accuracy of 95% and an average running time of 40 ms. [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.

Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed. [ABSTRACT FROM AUTHOR]

Vehicle and license plate detection plays an important role in intelligent transportation systems and is still a challenging task in real applications, such as on-road scenarios. Recently, Convolutional Neural Network (CNN)-based detectors achieve the state-of-the-art performance. However, it is difficult to efficiently detect the vehicle and license plate simultaneously in most cases. With a single network, the vehicle can affect the detection of the license plate due to the inclusion relation. In this paper, we propose an end-to-end deep neural network for detecting the vehicle and the license plate simultaneously in a given image, where two separate branches with different convolutional layers are designed for vehicle detection and license plate detection, respectively. In consideration of the license plate’s small size and fairly obvious features as well as the vehicle’s various size and rather complex features, the license plates are detected with low-level features and the vehicles are localized with multi-level features in corresponding convolutional layers. Moreover, a task-specific anchor design strategy is employed to obtain better predictions. Besides, the attention mechanisms and feature-fusion strategies are utilized to improve the detection performance of small-scale objects. A variety of experiments on real datasets and public datasets verify that our proposed method has fairly high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multi-national and multi-language detection, with either one-line or two-line layouts is presented. The YOLOv2 detector with ResNet feature extraction core was utilized for LP detection, and a new low complexity convolutional neural network architecture was proposed to classify LPs. Results show that the proposed approach achieves an average detection precision of 99.57%, whereas the country, language, and layout classification accuracy is 99.33%. [ABSTRACT FROM AUTHOR]

In recent years, measuring the speed of vehicles by a single camera has been done using several methods including the road geometric information, the difference in the number of image pixels of moving objects, and the homographic mapping of the motion vector from the image plane to the global coordinate plane. In this paper, we present a new method based on the geometry of the imaging system and the definition of solid angle. Our method does not require 3D modeling; It has a lower computational cost than the existing methods; And it has an accuracy comparable to the other approaches. In this method, we use the video images taken with a single camera on the road to extract the license plate in the image. Then, using the geometric information of the system and the distance travelled by vehicles the speed is computed. The average relative error of the proposed method is 3.23% and the mean absolute error is 1.32 km/h, which is comparable to the available algorithms. Furthermore, the computational cost of our method is less than the existing ones, which makes it suitable for implementing on embedded systems. [ABSTRACT FROM AUTHOR]

Recognition of characters from vehicle license plate plays a vital role in vehicle tracking, controlling, and maintaining traffic on the roads and high ways. This paper presents an automatic system to detect plate area and recognize characters. The research is carried out on Bangladeshi vehicle license plates because this is a demandable research area in the present age. The method has been implemented using the five consecutive steps such as license plate detection, extraction, character localization, extraction of characters, and recognition of extracted characters. We have introduced an algorithm to detect the Region of Interest (ROI) from the input image. It is performed by applying morphological operation and histogram analysis of vertical and horizontal projection profiles. A dynamic threshold is used to filter out both horizontal and vertical histogram values and ROI is extracted from the license plate image using different geometric properties such as area, bounding box, and aspect ratio. Character segmentation is performed by applying the method of connected component analysis and bounding box technology. Finally, character recognition is carried out using Support Vector Machine (SVM) classifier where extracted Histogram of Oriented Gradient (HOG) features are used as input. The proposed algorithm is applied to 630 images of license plates of different categories of vehicles and achieved 91% accuracy in the extraction of ROI and 94.6% accuracy in the extraction of characters. We have also achieved 100% accuracy in recognition of Bangla characters from a total of 4725 Bangla characters extracted from 630 images of license plates. [ABSTRACT FROM AUTHOR]

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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]

This paper aims to identify the license plates under difficult image conditions, such as low/high contrast, foggy, distorted, and dusty conditions. This paper proposes an efficient descriptor, multi-level extended local binary pattern, for the license plates (LPs) detection system. A pre-processing Gaussian filter with contrast-limited adaptive histogram equalization enhancement method is applied with the proposed descriptor to capture all the representative features. The corresponding bins histogram features for a license plate image at each different level are calculated. The extracted features are used as the input to an extreme learning machine classifier for multiclass vehicle LPs identification. The dataset with English cars LPs is extended using an online photo editor to make changes on the original dataset to improve the accuracy of the LPs detection system. The experimental results show that the proposed method has a high detection accuracy with an extremely high computational efficiency in both training and detection processes compared to the most popular detection methods. The detection rate is 99.10% with a false positive rate of 5% under difficult images. The average training and detection time per vehicle image is 4.25 and 0.735 s, respectively. [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]

License plate detection for vehicle identification is one of the key problems for traffic surveillance in urban areas. Most of the existing methods that can handle multiple license plates are country-specific as color information has been typically targeted. In this paper, we propose a real-time multiple license plate detection method feasible for multi-national vehicles having variable colors, sizes and geometrical attributes. A region-of-interest is initially identified for each vehicle using a fuzzy inference system based on the salient feature of its rear lights as license plates generally exist in a vicinity of these lights. Due to the abundance of edges within the license plate region, a local recursive analysis approach is utilized to locate the license plate candidate within each region-of-interest after tilt correction using a rear-light alignment technique. To verify the detected region as a true license plate, a unique combination of local image features is used to achieve high precision. The proposed method has been tested on 2200 images taken during various weather and illumination conditions to detect 5379 license plates out of 5945 available vehicles with 90.5% accuracy. The proposed approach outperforms the conventional and deep learning methods to achieve superior performance with the ability of being applied to multi-national vehicles. [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.