Vehicle License Plate Character Segmentation

Vehicle license plate (VLP) character segmentation is an important part of the vehicle license plate recognition system (VLPRS).This paper proposes a least square method (LSM) to treat horizontal tilt and vertical tilt in VLP images.Auxiliary lines are added into the image (or the tilt-corrected image) to make the separated parts of each Chinese character to be an interconnected region.The noise regions will be eliminated after two fusing images are merged according to the minimum principle of gray values. Then,the characters are segmented by projection method (PM) and the final character images are obtained.The experimental results show that this method features fast processing and good performance in segmentation.

A Practical Method of Car License Plate Character Segmentation Based on Morphology and Labeling

In this paper, a kind of practical image segmentation algorithm for segment characters from car license plate is presented, based on morphology and labeling. First by morphological operation, noise in the binary image of license plate can be greatly decreased. Then, by labeling, each connected pixel component is given a unique label. Finally, by the known data of license plate, each character is extracted correctly. The advantage of this method is that it can deal with plates with different sizes and connected characters plates, and inclined plates. The experiment results show that it is an effective way to extract characters from the license plate, and can be put into practical use.

Automatic Vehicle License Recognition Based on Video Vehicular Detection System

许可证字符抽取的传统的方法不能满足识别精确性的要求，速度由录像显示车辆的察觉系统。因此，基于多尺度的边，察觉和一个字符分割算法基于 Markov 随机回答模型的一个牌照本地化方法被介绍。实验的结果证明方法基于阀值由差别操作员和特性分割基于边察觉与传统的本地化方法相对照产出更精确的许可证特性抽取。，精确性在差的状况下面在更好的照明下面从 90% 增加到 94% ，它增加超过 5% 。当二个改进算法被使用时，自动许可证识别的精确性和速度遇见系统“甚至在吵闹的情形或不平的照明下面的 s 要求。

Deep Learning Based License Plate Number Recognition for Smart Cities

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

A novel license plate recognition method using HTD and VTD features

In this paper, a novel method of licence plate recognition (LPR) using the vertical traverse density (VTD) and horizontal traverse density (HTD) is presented. The neutral network algorithm using VTD and HTD features is also an innovation. In addition, a so called secondary recognition method which splits characters into different parts is developed. Experimental results show that it is a simple and fast algorithm, which meets the request of real time and nicety performances of LPR and thus has applied value in intelligence transportation system (ITS).

Libyan Licenses Plate Recognition Using Template Matching Method

License plate recognition (LPR) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. The work presented in this paper aims to create a computer vision system capable of taking real-time input image from a static camera and identifying the license plate from extracted image. This problem is examined in two stages: First the license plate region detection and extraction from background and plate segmentation to sub-images, and second the character recognition stage. The method used for the license plate region detection is based on the assumption that the license plate area is a high concentration of smaller details, making it a region of high intensity of edges. The Sobel filter and their vertical and horizontal projections are used to identify the plate region. The result of testing this stage was an accuracy of 67.5%. The final stage of the LPR system is optical character recognition (OCR). The method adopted for this stage is based on template matching using correlation. Testing the performance of OCR resulted in an overall recognition rate of 87.76%.

A Design Flow for Robust License Plate Localization and Recognition in Complex Scenes

In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization;2) character segmentation;and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.

开放场景中的高精度车牌识别算法

目前,限制条件下的车牌识别算法比较成熟,广泛应用于各种车牌识别系统。由于拍摄角度差异较大、车辆运动模糊等因素的影响,中文车牌识别仍具有较大的挑战性。针对上述问题,该文放弃单一的端到端深度学习的车牌识别方法,提出了一种检测、分类一体化的逐级车牌识别算法,采用逐级对象检测策略与字符分类相结合预测车牌的字符结果。在此基础上,提出一种多锚点字符位置回归算法,进一步精确回归所有车牌字符的局部区域位置信息。同时为了满足字符检测和字符分类的需求,解决现有车牌数据集类别不均衡的问题,该文贡献了一系列配套的车牌数据集。充分实验表明,该方法在不同数据集上都能达到目前的先进水平,并在公开数据集CCPD上准确率达到了99%,在开放场景中具备高精度和高鲁棒性。

复杂道路环境下的车辆牌照检测与识别

针对复杂道路环境中车牌因倾斜、模糊、遮挡导致图像定位检测效果不佳和识别精度低等问题,提出一种基于全局阈值的灰度二值化图像预处理方法,采用YoloV5l算法对后处理阶段的数据集进行定位检测和检测结果评估,并通过R-CNN模型识别定位检测后的车牌图像字符。结果表明:当训练过程持续到100轮次时,相比于Faster R-CNN算法,该模型检测的平均精度均值(mAP)提升9.2%,识别准确率提升17.33%,验证该方法检测和识别车牌的有效性与优越性。

基于STM32的车牌识别系统的实验设计及应用

以提高工程技术人员实践能力为目标,采用软硬件结合的方式,设计了基于STM32的车牌识别系统实验。以STM32单片机为实验平台,采用OV7670摄像头和TFT显示屏搭建了硬件系统;以C语言为基础,设计了软件流程。车牌识别实现包含图像预处理、二值化处理、识别车牌区域、字符分割和字符识别5个步骤。为解决光照和车牌表面洁净度不高对车牌识别准确度的影响,首先采用高斯滤波对图像预处理,之后利用跳变点设置上下边界阈值确定车牌识别区域,最后采用上下边缘投影法完成字符分割任务。实践表明,该实验提高了车牌识别检测精度,加深了工程技术人员对嵌入式技术的认知,提升了工程技术人员的实践能力和创新能力。

Wavelet packet based feature extraction and recognition of license plate characters

To study the characteristics of license plate characters recognition,this paper proposes a method for fea- ture extraction of license plate characters based on two-dimensional wavelet packet.We decompose license plate character images with two dimensional-wavelet packet and search for the optimal wavelet packet basis.This paper pre- sents a criterion of searching for the optimal wavelet packet basis,and a practical algorithm.The obtained optimal wavelet packet basis is used as the feature of license plate character,and a BP neural network is used to classify the character.The test- ing results show that the proposed method achieved higher recognition rate than the traditional methods.

基于LabVIEW和Vision Assistant的车牌识别系统设计

文章提出了一种基于LabVIEW和Vision Assistant的车牌识别方案。系统使用Vision Assistant的颜色阈值、高级形态学及光学字符识别等函数,实现了对车牌图像的预处理和车牌区域定位以及车牌字符分割、字符训练和识别,使用LabVIEW程序完成车牌的显示。使用数百张车牌图像对该系统进行验证,结果证实该方案是可行的,系统是可靠的,可以快速、准确定位和识别车牌。

融合出行拓扑与序列分析的车辆时空出行模式挖掘

城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车辆出行链,获得小汽车出行的时间、空间、频率和拓扑特征,根据各时段停留点构造车辆出行活动序列。其次,融合兴趣点(Point of Interest, POI)数据识别出行起讫点关联的土地利用特性作为停留点特征,在出行活动序列上应用k-modes聚类算法挖掘出常规通勤模式、特殊通勤模式、短时活动模式和外来办事模式这4类30种小汽车出行模式。最后,对每一类模式的群体规模、特征和典型出行行为进行详细地分析讨论。结果表明,95%的车辆出行活动可以用不多于3条边组成的简单拓扑结构表示,其中,约30%的车辆可构造出行活动序列,并用k-modes聚类算法有效分离出各类机动车全天出行的时空模式。工作日车辆出行主要表现为常规通勤模式,休息日则以短时活动模式为主。通过对个体车辆的微观行为分析,结合出行拓扑结构和出行活动序列进行出行模式的挖掘,能够全面地反映城市机动车出行的实际情况,为精细化机动车出行行为分析与管控策略制定提供理论支撑。

基于OpenCV的车牌识别系统设计

阐述使用OpenCV开源库处理计算机视觉图像,利用Python编程语言并结合卷积神经网络识别提取图像信息,以达到快速定位精确识别的目的。

基于轻量卷积神经网络的车牌定位识别方法

文中提出了一种新型基于轻量卷积神经网络的车辆车牌定位识别方法.采用基于改进的YOLOv3算法对车牌进行定位,基于免分割的轻量卷积神经网络LPRNet识别车牌字符.在车牌定位方面,改进YOLOv3网络的特征提取网络以降低设备要求,同时加入密集连接网络,增加对浅层特征信息的重复利用;在损失函数方面,引入DIOU损失函数,加快网络收敛以提高YOLOv3网络的定位精度;在车牌字符识别方面,采用基于免分割轻量卷积神经网络识别车牌字符,准确率高且保证了网络的轻量.结果表明:改进的YOLOv3网络算法的平均正确率降低了2.2%在CPU上检测速度达到了35帧/s,结合字符识别网络,总体检测速度达到27帧/s,满足实时性检测要求.

基于单字符注意力的全品类鲁棒车牌识别

复杂场景下的高精度车牌识别仍然存在着许多挑战,除了光照、分辨率不可控和运动模糊等因素导致的车牌图像质量低之外,还包括车牌品类多样产生的行数不一和字数不一等困难,以及因拍摄角度多样出现的大倾角等问题.针对这些挑战,提出了一种基于单字符注意力的场景鲁棒的高精度车牌识别算法,在无单字符位置标签信息的情况下,使用注意力机制对车牌全局特征图进行单字符级特征分割,以处理多品类车牌和倾斜车牌中的二维字符布局问题.另外,该算法通过使用共享参数的多分支结构代替现有算法的串行解码结构,降低了分类头参数量并实现了并行化推理.实验结果表明,该算法在公开车牌数据集上实现了超越现有算法的精度,同时具有较快的识别速度.

基于LabVIEW Vision的车牌识别系统设计

文中设计了一种基于LabVIEW Vision的车牌识别系统。该系统根据车牌颜色在HSL色彩空间上的特征,采用色彩阈值分割、腐蚀等图像处理方法将车辆图像转化成二值图,并在二值图中进行边缘检测,再根据检测到的车牌与背景间的边缘线的信息组建方程组,求出车牌边缘特征点的坐标,以确定车牌在图像中的位置。然后,以车牌在图像中的位置是否在指定区域内作为是否进行车牌识别以及当前车辆图像是否保存为车牌识别输入图像的触发条件。如果条件不满足,则系统将重新采集车辆图像,否则会根据车牌位置抓捕车牌,并采用均匀性度量法等方法对抓捕到的车牌进行二次处理,并将其转化成车牌二值图,再由OCR函数对该图进行字符识别。经实验验证,该系统具有较高的识别精确度与稳定性。

基于并行CNN和识别策略优化的车牌识别方法研究

为改善车牌自动识别系统的通用性,在一般卷积神经网络(CNN)的基础上,提出一种具有两个浅层独立子网络的CNN,且具有并行卷积层计算的功能.一个用于推理车牌的概率;另一个利用线性激活对仿射参数进行回归.支持对汽车(包括公交车和卡车等)、摩托车等不同类型的交通工具牌照的检测识别.此外,使用基于YOLO v3的车牌字符识别模块,并施加了一系列的优化策略,实现对车牌中汉字的准确读取.实验结果表明所提方法的识别精度优于一些同类优秀方法,在AOLP数据集上的车牌检测准确率达到98.9%,在CLPD数据集上的字符识别准确率达到96.2%.所提方法有助于促进智能交通系统的进一步发展.

基于集成学习的信号控制交叉口排队长度估计

基于电子警察(LPR)数据和网联车辆轨迹数据,提出了一种基于集成学习的信号控制交叉口排队长度估计方法。通过分析不同数据条件下估计方法的适用条件和精度水平,运用随机森林方法设计集成学习器,并构建电子警察和网联车辆轨迹感知信息及不同方法估计结果和真实排队长度之间的非线性映射关系。仿真结果表明:本方法的平均绝对误差为1.3 m·周期^(-1)·车道^(-1),平均绝对百分比误差为1.4%。

基于深度学习的车牌定位与字符识别

智慧交通需要通过深度学习来提高车牌定位与字符识别的能力,通过大数据支持能有效地监控城市交通,给市民生活带来更多的安全感。在长期的经济发展过程中,人工智能在提高社会运行效率上有积极的作用,是推动智能城市发展的重要手段。本文通过深度学习的方式研究了车牌定位与字符识别。首先,通过色彩分割、小波变换、遗传算法、灰度图像纹理特征分析四种方法对车牌定位进行研究。然后,利用图像字符矫正预处理、图像字符分割提取、字符归一化预处理三个部分对车牌字符处理方法进行阐述。通过深度学习领域的算法能切实提高交通领域的发展效率,保障市民出行安全。通过车牌定位与字符识别能有效地读取交通领域的车辆信息,这样的方式能更好地对提升城市交通运行效率提供助力。