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首页> 外文期刊>Expert systems with applications >An Intelligent Strategy For Checking The Annual Inspection Status Of Motorcycles Based On License Plate Recognition
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An Intelligent Strategy For Checking The Annual Inspection Status Of Motorcycles Based On License Plate Recognition

机译：基于车牌识别的摩托车年检状态智能检测策略

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摘要

License plate recognition techniques have been successfully applied to the management of stolen cars, management of parking lots and traffic flow control. This study proposes a license plate based strategy for checking the annual inspection status of motorcycles from images taken along the roadside and at designated inspection stations. Both a UMPC (Ultra Mobile Personal Computer) with a web camera and a desktop PC are used as hardware platforms. The license plate locations in images are identified by means of integrated horizontal and vertical projections that are scanned using a search window. Moreover, a character recovery method is exploited to enhance the success rate. Character recognition is achieved using both a back propagation artificial neural network and feature matching. The identified license plate can then be compared with entries in a database to check the inspection status of the motorcycle. Experiments yield a recognition rate of 95.7% and 93.9% based on roadside and inspection station test images, respectively. It takes less than 1 s on a UMPC (Celeron 900 MHz with 256 MB memory) and about 293 ms on a PC (Intel Pentium 4 3.0 GHz with 1 GB memory) to correctly recognize a license plate. Challenges associated with recognizing license plates from roadside and designated inspection stations images are also discussed.
机译：车牌识别技术已成功应用于偷车管理，停车场管理和交通流控制。这项研究提出了一种基于车牌的策略，用于从沿路边和指定检查站拍摄的图像中检查摩托车的年度检查状态。具有网络摄像头的UMPC（超移动个人计算机）和台式PC均用作硬件平台。图像中的车牌位置通过集成的水平和垂直投影来识别，这些投影使用搜索窗口进行扫描。此外，利用字符恢复方法来提高成功率。使用反向传播人工神经网络和特征匹配来实现字符识别。然后可以将识别出的车牌与数据库中的条目进行比较，以检查摩托车的检查状态。根据路边和检查站的测试图像，实验的识别率分别为95.7％和93.9％。在UMPC（Celeron 900 MHz，带256 MB内存）上花费不到1 s，在PC（Intel Pentium 4 3.0 GHz，1 GB内存）上花费大约293 ms。还讨论了与从路边和指定检查站图像中识别车牌相关的挑战。

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