Query: license plate recognition

title: Automatic Number Plate Recognition for Indonesian License Plate by Using K Nearest Neighbor Algorithm

title: Automatic Vehicle License Plate Recognition Using Optimal K Means With Convolutional Neural Network for Intelligent Transportation Systems
abstract: Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS) The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning based VLPR model using optimal K means (OKM) clustering based segmentation and Convolutional Neural Network (CNN) based recognition called OKM CNN model. The proposed OKM CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM CNN model over other compared methods in a considerable way.

title: Multinational License Plate Recognition Using Generalized Character Sequence Detection
abstract: 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.

title: Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment
abstract: License plate recognition systems are widely used in modern smart cities, such as toll payment systems, parking fee payment systems and residential access control. Such electronic systems are not only convenient for people's daily life, but also provide safe and efficient services for managers. License plate recognition algorithm is a mature but imperfect technology. The traditional location recognition algorithm is easily affected by light, shadow, background complexity or other factors, resulting in the failure to meet the application of real scenes. With the development of deep learning, the license plate recognition algorithm can extract deeper features, thus greatly improving the detection and recognition accuracy. Therefore, this paper discusses the application of deep learning in license plate recognition, and the main work is as follows: 1) Introduce the most advanced algorithms from the three main technical difficulties: license plate skew, image noise and license plate blur; 2) According to the process, the deep learning algorithms are classified into direct detection algorithms and indirect detection algorithms, and the advantages and disadvantages of the current license plate detection algorithms and character recognition algorithms are analyzed; 3) The differences in data sets, workstation, accuracy and time of different license plate recognition systems are compared; 4) Compare and illustrate the existing public license plate datasets according to the number of pictures, resolution and environmental complexity, and make a prospect for the future research direction of license plate recognition.

title: Weakly Supervised Character Detection for License Plate Recognition
abstract: Automatic Licence Plate Recognition (ALPR) is an essential task in the context of intelligent transportation systems. In a typical ALPR pipeline, the last stage receives as input a cropped license plate region and outputs the string with the plate characters. This paper presents a Weakly Supervised Character Detection (WSCD) approach that requires only string level an notations (as in generic text recognition methods) but is able to detect characters individually (as in detection based methods, which require character level annotations) The proposed method is evaluated in five distinct datasets and present very competitive results against other state of the art methods.

title: Automatic License Plate Recognition via sliding window darknet YOLO deep learning
abstract: is an important research topic in the intelligent transportation system and image recognition fields. In this work, we address the problem of car license plate detection using a You Only Look Once (YOLO) darknet deep learning framework. In this paper, we use YOLO's 7 convolutional layers to detect a single class. The detection method is a sliding window process. The object is to recognize Taiwan's car license plates. We use an AOLP dataset which contained 6 digit car license plates. The sliding window detects each digit of the license plate, and each window is then detected by a single YOLO framework. The system achieves approximately 98.22% accuracy on license plate detection and 78% accuracy on license plate recognition. The system executes a single detection recognition phase, which needs around 800 ms to 1 s for each input image. The system is also tested with different condition complexities, such as rainy background, darkness and dimness, and different hues and saturation of images.

title: Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolution Neural Networks
abstract: Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work primarily focused on how to generate adversarial examples closed to source images, by introducing pixel level perturbations into the whole or specific part of images. In this paper, we propose an evasion attack on CNN classifiers in the context of License Plate Recognition (LPR) which adds predetermined perturbations to specific regions of license plate images, simulating some sort of naturally formed spots (such as sludge, etc. Therefore, the problem is modeled as an optimization process searching for optimal perturbation positions, which is different from previous work that consider pixel values as decision variables. Notice that this is a complex nonlinear optimization problem, and we use a genetic algorithm based approach to obtain optimal perturbation positions. In experiments, we use the proposed algorithm to generate various adversarial examples in the form of rectangle, circle, ellipse and spots cluster. Experimental results show that these adversarial examples are almost ignored by human eyes, but can fool HyperLPR with high attack success rate over 93% Therefore, we believe that this kind of spot evasion attacks would pose a great threat to current LPR systems, and needs to be investigated further by the security community.

title: Link based traffic state estimation and prediction for arterial networks using license plate recognition data
abstract: License plate recognition (LPR) data are emerging data sources in urban transportation systems which contain rich information. Large scale LPR systems have seen rapid development in many parts of the world. However, limited by privacy considerations, LPR data are seldom available to the research community, which lead to huge research gap in data driven applications. In this study, we propose a complete solution using LPR data for link based traffic state estimation and prediction for arterial networks. The proposed integrative data driven framework provides the inference of both cycle maximum queue length states and average travel times of links using LPR data from a subset of intersections in an arterial network. The framework contains three novel data driven sub components that are highly customized based on the characteristics of LPR data, including: a traffic signal timing inference model to find signal timing information from the LPR timestamp sequences; a light weighted queue length approximation model to estimate lane based cycle maximum queue lengths and a network wide traffic state inference model to perform network level estimation and prediction using partially observed data. This study exploits and utilizes the unique features of LPR data and other similar vehicle re identification data for urban network wide link based traffic state estimation and prediction. A six days' LPR dataset from a small road network in the city of Langfang in China and a more comprehensive link level field experiment dataset are used to validate the model. Numerical results show that the framework provides good estimation and prediction accuracy. The proposed framework is efficient and calibration free, which can be easily implemented in urban networks for various real time traffic monitoring and control applications.

title: Toward an Optimized Neutrosophic k Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM AVLPR)
abstract: The present paper proposes a new methodology for license plate (LP) recognition in the state of the art of image processing algorithms and an optimized neutrosophic set (NS) based on genetic algorithm (GA) First of all, we have performed some image processing techniques such as edge detection and morphological operations in order to utilize the (LP) localization. In addition, we have extracted the most salient features by implementing a new methodology using (GA) for optimizing the (NS) operations. The use of (NS) decreases the indeterminacy on the (LP) images. Moreover, k means clustering algorithm has been applied to segment the (LP) characters. Finally, we have applied connected components labeling analysis (CCLA) algorithm for identifying the connected pixel regions and grouping the appropriate pixels into components to extract each character effectively. Several performance indices have been calculated in order to measure the system efficiency such as accuracy, sensitivity, specificity, dice, and jaccard coefficients. Moreover, we have created a database for all detected and recognized (LP) for testing purposes. Experimental results show that the proposed methodology has the ability to be suitable for both (Arabic Egyptian) and English (LP) The proposed system achieves high degree of recognition accuracy for the whole system according to the following case studies; (i) for a high resolution Egyptian (LP) the proposed system achieves about 96.67% accuracy of correct recognition, (ii) for a low resolution corrupted English (LP) the proposed system achieves about 94.27% accuracy. In addition, we have applied the proposed system on some sort of image disturbance i.e. (flash in image, external noise, and illumination variation) the proposed system achieves about 92.5% accuracy of correct identification. However, traditional methods achieve about 79% accuracy of correct identification in the presence of such image degradations. This reflects how the proposed system is generalized, optimized, and proposes high degree of recognition accuracy.

title: An embedded automatic license plate recognition system using deep learning
abstract: A system to automatically recognize vehicle license plates is a growing need to improve safety and traffic control, specifically in major urban centers. However, the license plate recognition task is generally computationally intensive, where the entire input image frame is scanned, the found plates are segmented, and character recognition is then performed for each segmented character. This paper presents a methodology for engineering a system to detect and recognize Brazilian license plates using convolutional neural networks (CNN) that is suitable for embedded systems. The resulting system detects license plates in the captured image using Tiny YOLOv3 architecture and identifies its characters using a second convolutional network trained on synthetic images and fine tuned with real license plate images. The proposed architecture has demonstrated to be robust to angle, lightning, and noise variations while requiring a single forward pass for each network, therefore allowing faster processing compared to other deep learning approaches. Our methodology was validated using real license plate images under different environmental conditions reached a detection rate of 99.37% and an overall recognition rate of 98.43% while showing an average time of 2.70 s to process $1024 \times 768$ 1024 x 768 images with a single license plate in a Raspberry Pi3 (ARM Cortex A53 CPU) To improve the recognition accuracy, an ensemble of CNN models was tested instead of a single CNN model, which resulted in an increase in the average processing time to 4.88 s for each image while increasing the recognition rate to 99.53% Finally, we discuss the impact of using an ensemble of CNNs considering the accuracy performance trade off when engineering embedded systems for license plate recognition.