实时的车牌识别系统 中英文.docx

VISL02年项目一种实时车牌识别（LPR）的 系统在完成了由酒吧，母鸡罗恩指导单位约哈难埃雷兹该系统一个典型的模式:摘要这个项目的目的是建立从汽车板在门入口处时，例如A区牌照时停车一个真正的应用程序，它已承认。 该系统 具有视频摄像机的普通PC机，渔获量的视频帧，其中包括一个明显的汽车牌照和处理它们。一旦发现车牌，它 的数字确认，并显示在用户界面或数据库核对一。形象的重点是设计一个单一的算法车牌从用于提取，分离板的 特点及识别单个字符。背景：目前已在实验室过去类似的项目。 包括项目实施的整个系统。这个项目的目的首先是改善方案的准确度，并尽 可能其时间复杂度。该实验室的所有项目在过去。根据精度不佳的测试中，我们就程序设置的45个影像，我 们用我们的成功，并只有在特定的条件感到满意。出于这个原因，除了再次从非常罕见的情况下，整个程序写。简要说明执行情况：我们的车牌识别系统可大致分为以下框图。框图全球系统。另外这个进程可以被看作是减少或地方的牌照抑制有害信息从携带信息的信号，这里是一个视频序列包含大量无 关信息的特点，形式抽象符号的研究。光学字符识别（OCR）已采用神经网络技术，采用神经元在输出层的前馈网络的3层，200个神经元在20输入层，中间神经元在10层，。 我们保留了神经网络数据集图像用在项目的先例，其中包括238位第我们的算法的详细步骤说明如下图：框图程序的子系统。这里介绍捕获帧的一个给定的产出上面所述的主要步骤:示例捕获帧黄色区域捕获的帧过滤捕获帧地区扩张黄色车牌区域确定氨角度的变换板的使用改进的LP地区调整唱片轮廓列和图调整唱片轮廓线条和图唱片作物灰度唱片唱片二值化，均衡使用自适应阈值二进制唱片归唱片确定使用的LP水平轮廓图像总和先决行归唱片轮廓调节字符分割使用的山峰到山谷方法侦扩张型数位影像调整数字图像水平轮廓-线和图调整的数字图像轮廓*调整大小的数字图像LP Number = 6562710OCR的数字识别的神经网络方法工具该方案实施开发了基于Matlab。 一个演示方案，用户可以看到所有的算法步骤的不同，设置水平得到他想要 的细节，并演示速度也是29的书面图上所示。 该演示可以启动，停止或暂停。 在其最新版本的演示包括45 图像上对算法进行了成功。重要的是要注意到的仿真速度并不反映''命令真正的速度”停顿了整个算法方案以来已 插入，和图像加载本身需要时间。演示图形用户界面:lP HJFiiflrfii EgUif'LTMd初L WSi 一间CHridw 点 |iwi¥ «u- Gagu Sum 白 咿一个非常有用的免费软件的大部分功能，允许测试的综合MATLAB图像处理的源下载The MathWorks公司的 网站和它的一个链接出现在页面的最后这一点。在这个页面中第一张照片是从高科技解决方案授权的网站与他们。结论和未来工作第一个结论是，什么是微不足道的项目对于人眼的任务，可能会出现一个非常困难的计算机，计算机视觉，但仍 是非常强大的，许可实施本执行非常有用的操作，我们作为一个。该方案在算法中使用已经过测试，证明是准确，高效，但还是有一些情况时，他们失败。以下是最重要的问题， 我们注意到：- 最重要的问题是神经网络数据集大小：如果在未来扩大的实现，这将在很大程度上提高了算法的准确 度。- 黄色区域的候选选择算法在过滤图像有时会失败，主要的改善将是固定的算法改进统计此参数用于。- 一般来说，所有的统计应该进一步完善固定参数通过进行更多的测试。- 黄色区域提取算法有时会失败，这将是一个好主意，在今后的执行加入该算法的补充，是''立足于事实， 那行中的数字板块位于形象有一个明确的”“签名''这相当于在强大的灰度变化在某种程度上"，'' 常规”“间隔 这使得它的形象通常可以区分他们在从其他线路，或至少预先选择一些位置在哪里看得更远。- 一般来说，决定算法应该得到改善，一种方法来检测错误，并作出决定应制定流动循环，例如，如果 有规范，多名候选人为LP的位置，满足他们的每一个测试根据预先定义的补充规范，或怀疑，在情况下， 当确定的数字，那是当概率的最好的猜测是正确的决定作出以下是一些门槛时，系统应该拒绝。承认我们感谢我们的项目主管，该实验室。总工程师加利亚的埃雷兹，对他的帮助和指导整个工作。我们也感谢 Ollendorf密涅瓦中心项目资金支持这一点。相关文档 图片 完整的文件PROJECTS AT VISL FINISHED IN 2002A Real-time vehicle License Plate Recognition (LPR) Systemby Bar-Hen RonSupervised by Johanan ErezA typical schema for the system:AbstractThe purpose of this project was to build a real time application which recognizes license plates from cars at a gate, for example at the entrance of a parking area. The system, based on regular PC with video camera, catches video frames which include a visible car license plate and processes them. Once a license plate is detected, its digits are recognized, displayed on the User Interface or checked against a database. The focus is on the design of algorithms used for extracting the license plate from a single image, isolating the characters of the plate and identifying the individual characters.The background:There have been similar past projects at the Lab. including projects which implemented the whole system. The purpose of this project was first and foremost to improve the accuracy of the program, and whenever possible its time-complexity. All the past projects at the Lab. had poor accuracy according to the tests we made on the set of 45 images we used in our program and were successful only when particular conditions were satisfied. For this reason, except from very rare cases the entire program was written again.Brief description of the implementation:Our license plate recognition system can be roughly broken down into the following block diagram.L he seven cliaraeters cL'thc license plate7 images with the charactersSub image containingonly 11 ic license pl tileSingle imageInput video sequenceBlock diagram of the global system.Alternatively this progression could be viewed as the reduction or suppression of unwanted information from the information carrying signal, here a video sequence containing vast amounts of irrelevant information, to abstract symbols in the form of the characters of a license place.The Optical Character recognition (OCR) has been made using the Neural Network technique, using a feed-forward network with 3 layers, 200 neurons in the input layer, 20 neurons in the middle layer, and 10 neurons in the output layer. We kept the Neural Network dataset used in a precedent project which includes 238 digit image s.The detailed steps of our algorithm are described in the following diagram:Block diagram of the program subsystems.Here are described the outputs of the main steps described above on a given captured frame:Example of a captured frameCaptured frame with yellow regions filteredCaptured frame with yellow regions dilatedLicense plate regionDetermining the angle of the plate using the Radon transformImproved LP regionAdjusting the LP Contours - Columns Sum GraphAdjusting the LP Contours - Lines Sum GraphLP CropGray scale LPLP binarization and Equalization using an adaptive thresholdBinary LPNormalized LPDetermining the LP horizontal contours usingthe sum of the lines of the precedent imageNormalized LP with contours adjustedCharacter Segmentation using the peaks-to-ValleyS methodDilated digit imageAdjusting digit images horizontal contours - Line sum graphContours adjusted digit imageResized digit imageLP Number = 6562718OCR digits recognition using the Neural Network methodToolsThe implementation of the program was developed on Matlab. A demo program on which the user can see all the steps of the different algorithms, set the level of details he wants to get, and the speed of the demo was also written as shown on Figure 29. The demo can be started, stopped, or paused. In its current version the demo includes 45 images on which the algorithm was successful. It is important to notice that the speed of the simulation does not reflect the real speed of the whole algorithm since “pause” commands has been inserted into the program, and the loading of images itself takes time.The Demo Graphical User Interface:A very useful freeware permitting to test most of the Matlab image processing integrated functions was downloaded from the Mathworks site and a link to its source appear at the end of this page.The first picture in this page were taken from Hi-Tech Solutions site with their authorization.Conclusions and future worksThe first conclusion is that what is trivial for the human eye may appear a very difficult task for the computer, but still computer vision can be very powerful and permit to perform very useful operations as the one we implemented in this project.The algorithms used in the program have been tested and proved to be accurate and efficient, but still there are cases when they fail. Following are the most important problems we noticed:The most important problem is the Neural Network dataset size: if enlarged in future implementations, it will largely improve the accuracy of the algorithm.- The Candidate selection algorithm in the yellow regions filtered image sometimes fails, and the main improvement would be to refine the statistically fixed parameters used in this algorithm.- In general, all the statistically fixed parameters should be refined by performing more tests.- The yellow region extraction algorithm sometimes fail, and it would be a good idea in future implementation to join it to the supplementary algorithm which is based on the fact that the lines where the number plate is located in the image have a clear ""signature"" which corresponds to strong grey level variations at somehow ""regular"" intervals which makes it usually possible to distinguish them from other lines in the image, or at least to pre-select some positions where to look further.- Generally, the decision algorithms should be improved, and a way to detect error and to make decisions flow circular should be developed, for example, if there are multiple candidates for LP location that satisfies the criterions, testing each one of them according to predefinedsupplementary criterions, or, in cases of doubt when identifying the digits, that is when the probability of the best guess being correct is below some threshold, the system should refuse to make a decision.AcknowledgmentWe are grateful to our project supervisor, the Lab. chief engineer Johanan Erez , for his help and guidance throughout this work. We are also grateful to the Ollendorf Minerva Center Fund for supporting this project.Related documentation Images Full documentation