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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication
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A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

机译：基于视觉的基于机器牌照认证的障碍物进入控制机器学习方法

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摘要

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.
机译：自动车辆牌照识别是智能车辆访问控制和监视系统的重要组成部分。随着车辆数量的增加，开发有效的实时系统以自动识别车牌非常重要。计算机视觉技术通常用于此任务。然而，由于在这样的系统中既需要高精度又需要低处理时间，所以这仍然是一个具有挑战性的问题。在这里，我们提出了一种识别车牌的方法，力求在这两个要求之间找到平衡。所提出的方法包括两个阶段：检测和识别。在检测阶段，对图像进行处理，以便识别感兴趣的区域。在识别阶段，使用定向梯度直方图方法从感兴趣区域中提取特征。这些特征随后被用于训练人工神经网络以识别车牌中的字符。实验结果表明，与现有方法相比，该方法具有较高的准确性和较低的处理时间，表明该方法适用于实时应用。

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