A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
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DOI：
10.3390/a14110317
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发表时间：
2021-10
期刊：
影响因子：
2.3
通讯作者：
A. Ahmed;S. Ahmed
中科院分区：
文献类型：
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作者：
A. Ahmed;S. Ahmed
车牌自动识别技术在停车场管理、交通管理、收费、智能交通等领域有着广泛的应用。尽管这项技术的重要性，现有的ANPR方法遭受的准确识别号码板，由于其不同的大小，方向和形状在世界各地的不同地区。在本文中，我们通过使用机器学习（ML）模型实施智能汽车牵引管理的案例研究来研究这些挑战。所开发的基于移动的系统使用不同的方法和技术来提高实时识别车牌的准确性。首先，我们开发了一种算法，以准确地检测车牌的车身上的位置。然后，提取平板的边界框并将其转换为灰度图像。其次，我们应用一系列的过滤器来检测灰度图像中的字母数字字符的轮廓。第三，检测到的字母数字字符的轮廓被送入一个K-近邻（KNN）模型，以检测实际的号码盘。我们的模型在识别全球不同地区的车牌时达到了95%的整体分类准确率。用户界面开发为Android移动的应用程序，允许执法人员拍摄被拖车辆的照片，然后自动实时记录在车辆牵引管理系统中。该应用程序还允许车主搜索他们的汽车，检查案件状态，并支付罚款。最后，我们使用各种性能指标（如分类精度，处理时间等）评估了我们的系统。我们发现，我们的模型在整体处理时间方面优于一些最先进的ANPR方法。
Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.