Search

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]

Detecting license plates has never been easy, particularly with the proliferation of sophisticated radars on highways and roads. By 2021, the gendarmerie and National Security Road control agents will have access to more than 1 billion smart traffic radars worldwide. This research presents a revolutionary technique for detecting and recognizing Arabic and Latin license plates. After assembling the gathered images to create a novel dataset, we utilized YOLO v7 to locate and identify the number plate in the image as the first step of the suggested procedure. Before the dataset was fed to the detection system, it was manually labeled. Afterward, we improved the recognized license plate using machine learning methods. To do this, we used kernel methods as well as thresholding to get rid of the extra vertical lines on the plate. After that, we employed Arabic OCR along with Easy OCR methods to decipher the Latin and Arabic characters on the number plate. Eventually, the proposed method achieved an F1 score of 98%,with a precision and recall of 97% and 98%, respectively. We also obtained an accuracy of 99% for image segmentation. The segmentation and detection results from the suggested strategy have shown satisfactory results. [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.

With the great success of deep learning and IoT techniques, many methods with GPU for License Plate Detection (LPD) have attracted remarkable attention in recent times. However, GPUs are not always equipped for video surveillance equipment due to device costs in a distributed edge-computing environment. Thereby, most methods of LPD on edge devices have poor running performance without GPUs. In this paper, we propose a novel hybrid methodology for license plate detection, which can accurately and quickly locate license plates in complex visual surveillance scenes without GPUs. As the background colors in China's license plates are specific, blue or green, a well-designed color filtering and Sobel detector are used to preprocess images to eliminate the complex background, which rapidly speed up the process of license plate detection. Then, we employ heuristic Simple Moving Average (SMA) search method to filter out the license plate area that meets the definite conditions from the candidate areas. In addition, Support Vector Machine(SVM) and trained Retina model are introduced to further detect the position of license plate for pictures without any candidate areas to improve the accuracy. Finally, we evaluate the proposed method on Chinese City Parking Dataset(CCPD), which is China's largest public license plate data set. Results demonstrate that our method is 312.37% faster than the original Retina model on devices without GPUs and still achieves the precision of 97.95%. These results show that the proposed method outperforms other methods on detecting Chinese blue license plates on CPU devices. [ABSTRACT FROM AUTHOR]

License plate detection is important for several applications such as law enforcement, toll collection, and parking management, as it enables the automatic identification and tracking of vehicles based on their unique license plate numbers, thereby improving efficiency and accuracy in these processes is a challenging task. In this research, an investigation was conducted on license plate detection using a deep learning-based approach. The approach utilized a Yolo-based algorithm to identify and extract license plates from images. The process involved the collection and preparation of a large dataset of labeled license plate images, which were used to train a CNN-based model using the Yolo algorithm through supervised learning. The accuracy of the trained model was then evaluated using a validation set of images to identify areas for improvement. Furthermore, the trained model was tested on new, unseen images to evaluate its performance in real-world scenarios. Finally, to ensure the effectiveness of the model, it was evaluated with additional images from outside of the dataset. As experimental results indicate, the model is an effective model and has high accuracy for license plate detection. [ABSTRACT FROM AUTHOR]

It has become a mandatory requirement for electric bike riders to wears helmet on riding. To automatically check if the electric bike rider wears helmet, a helmet and license plate detection approach based on improved YOLOv5m model is herein proposed, which can locate and recognize the license plate of the unhelmeted rider, so as to track down the violators. The model is trained with self-built dataset, uses DIOU loss function instead of GIOU loss function, and uses DIOU_NMS to replace weighted NMS so as to enhance the recognition ability for dense cycling scenes. Meanwhile, the ECA attention mechanism is added to the Backone and the Neck parts to im- prove the recognition accuracy for small and medium-sized targets. Then, the K-means algorithm is used to re- cluster the anchor frame size. Finally, the Mosaic data enhancement method is improved. The experimental results show that the mAP of the proposed approach is 92.7%, which is 2. 15,5. 7, and 6. 9 percentage points higher than the original YOLOv5m, YOLOv4 tiny, and Faster RCNN, respectively. It can be concluded that the improved YOLOv5m model can accurately recognize rider's helmet and electric bike's license plate. [ABSTRACT FROM AUTHOR]

Automatic license plate recognition which has many applications in intelligent transportation systems has three main steps, License Plate Detection (LPD), segmentation, and character recognition. The first step, LPD is the main step. This is because the accuracy of other steps depends on this step. While LPD has many challenging problems such as illumination changing, weather conditions, vehicle position, number of cars in each image, etc. In this paper, an algorithm for LPD is proposed. In the proposed technique, to remove noises from input images mean filtering is used. After that, the differences between the filtered image and input image are computed and it is converted to a black and white image. Finally, after applying edge detection and closing and opening morphological operations, license plate candidates are detected. The experimental results on a real Iranian dataset show that about 96.16% of license plates are localized. In addition, results show that the mean filter which has been used for noise removal is the most time-consuming kernel in the proposed algorithm. This important kernel on the GPU platform using CUDA parallel programming model is implemented. The experiments show that speedups of up to 14.54× and 10.77× for kernel- and application-level are achieved. [ABSTRACT FROM AUTHOR]

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]

In literature, various metaheuristic approaches such as Genetic Search Algorithm (GSA), has been adopted for finding the sub-optimal solution to a wide range of optimization problems. The main challenges in adopting GSA is the formulation of a proper fitness function which provides a measure of evaluating the generated candidate solutions, as the subsequent steps in the searching process would mainly be based on the quality of the previous and current solutions. As such, this is a highly crucial step in the successful application of GSA. However, in most of the applications, the construction of the suitable fitness function is difficult due to lack of analytical relations between the GSA parameters and the fitness of the solution. In this paper, a GSA approach of using shallow artificial neural network as a surrogate fitness function is proposed to alleviate such difficulties in the application of the GSA. The license plate detection problem is selected as a case study. For this problem, a new set of features which is called structured Histogram of Oriented Gradients (sHOG) is proposed in order to improve the overall performance of the license plate detection problem. The sHOG features were used to train the shallow ANN which assigns a degree of confidence score to the candidate regions and hence guide the GSA search to sub-optimal solution in the search space of a given input image. The performance of the proposed approach was evaluated on a private and public license plates datasets and results proves that it can archive an IOU detection rate of up to 98.74% on the private dataset and 91.66% cross database performance on the public dataset. [ABSTRACT FROM AUTHOR]

Recently, automatic license plate recognition (ALPR) has drawn much of attention from researchers due to the impressive performance of deep learning (DL) techniques. While a large number of methods for ALPR have been investigated, there are a few attempts emphasizing efficient yet accurate models for facilitating the deployment on traditional CPU boxes or low resource devices. In the present work, we propose lightweight and effective deep convolutional neuron networks to address the problems of license plate detection and recognition. Differing from traditional DL methods, the proposed models discard the use of max-pooling modules, belong to a single-phase object detector, and consist of alternating convolutional layers and Inception residual networks. Different strategies for character prediction are also studied and deeply discussed. To have more insight of convolutional features, we provide a sequence of visual snapshots or slices of feature maps learned by the network at different layers for a given input image. These data are useful to explain the model's behavior for the task of character recognition. Extensive experiments conducted on two public datasets, CCPD and AOLP, showed a promising improvement of LP detection and recognition accuracy. Interestingly, the full system can work on low-resource CPU machines with a real-time speed. [ABSTRACT FROM AUTHOR]

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions. The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype. Using any of the previous versions will give moderate results but with very low speed. Hence, a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols. The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques. Also, a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns. Various image samples with a wide range of scale, orientation, and illumination conditions have been experimented with to verify the effect of the new improvements. Encouraging results with 97.55% detection precision have been reported using the recent challenging public Chinese City Parking Dataset (CCPD) outperforming the author of the dataset by 3.05% and the state-of-the-art technique by 1.45%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 a critical technology for intelligent transportation systems. Most existing ALPR methods are focused on specific application scenarios. Although there are a few methods that focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient ALPR (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems with high efficiency. We design a light LPD structure based on efficient object detection methods and use anchor-free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations and a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method on five datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, our method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficient-alpr-unconstrained. [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.

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

Automatic license plate recognition plays an important role in intelligent transportation systems and is of great significance. However, at present, most current approaches are only concerned of license plate recognition under restrictive conditions, where the license plates are shot in a frontal view and under good light conditions. These approaches are not robust enough in real-world complex capture scenarios, such as uneven light condition or oblique shooting angle. In order to improve the robustness of recognizing license plates under complex capture scenarios, a robust license plate detection network (CA-CenterNet) is proposed in this paper, together with a segmentation-free network (CNNG) for the recognition of license plate characters. CA-CenterNet can detect not only the center of each license plate, but also four vectors pointing to the four corners of the corresponding license plate, regardless of the rotation and distortion of the license plates, which gives us the possibility to rectify the distorted license plates in the source images. Then, CNNG can accurately identify the characters in the detected license plates without character segmentation. Experimental results prove that our automatic license plate recognition system has good performance in real-world complex capture scenarios and outperforms current license plate recognition models. [ABSTRACT FROM AUTHOR]

Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a high-performance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a field-programmable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys’s design compiler tool was 482.583mW. [ABSTRACT FROM AUTHOR]

License plate (LP) detection in the wild remains challenging due to the diversity of environmental conditions. Nevertheless, prior solutions have focused on controlled environments, such as when LP images frequently emerge as from an approximately frontal viewpoint and without scene text which might be mistaken for an LP. However, even for state-of-the-art object detectors, their detection performance is not satisfactory for real-world environments, suffering from various types of degradation. To solve these problems, we propose a novel end-to-end framework for robust LP detection, designed for such challenging settings. Our contribution is threefold: (1) A novel information-theoretic learning that takes advantage of a shared encoder, an LP detector and a scene text detector (excluding LP) simultaneously; (2) Localization refinement for generalizing the bounding box regression network to complement ambiguous detection results; (3) a large-scale, comprehensive dataset, LPST-110K, representing real-world unconstrained scenes including scene text annotations. Computational tests show that the proposed model outperforms other state-of-the-art methods on a variety of challenging datasets. [ABSTRACT FROM AUTHOR]