利用单板计算机技术评估影响基于物联网的实时车牌识别的因素

P. Netinant, Siwakron Phonsawang, Meennapa Rukhiran
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引用次数: 0

摘要

可靠且经济高效的车牌识别(LPR)系统可在实际应用中加强安全、交通管理和自动收费。本研究通过评估摄像头角度、物体速度和距离对实时 LPR 系统功效的影响,探讨了提高 LPR 系统准确性和可靠性的最佳独特配置。本研究提出了物联网(IoT)LPR 框架,并将其应用于单板计算机(SBC)技术,如 Raspberry Pi 4 平台,同时使用先进的 OpenCV 和 OCR-Tesseract 算法安装高分辨率网络摄像头。研究致力于模拟实时 LPR 系统的常见部署场景,并利用 SBC 的计算能力和网络摄像头的成像能力进行全面测试。测试过程不仅全面,而且细致,确保了系统在各种操作环境下的可靠性。我们在不同的角度、速度和距离下进行了上百次重复的广泛实验。对数据精确度、召回率和 F1 分数的评估表明了识别泰国车牌的准确性。结果表明,摄像头角度接近 180°,可显著减少透视失真,从而提高精确度。较低的车速(<10 公里/小时)和较短的距离(<10 米)也能减少运动模糊,提高图像清晰度,从而提高识别准确率。从较短距离(约小于 10 米)捕获的图像对于高分辨率字符识别而言更为准确。这项研究为利用基于物联网的实时 LPR 系统的 SBC 技术做出了重大贡献,以实现实用、准确和经济高效的实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Factors Shaping Real-Time Internet-of-Things-Based License Plate Recognition Using Single-Board Computer Technology
Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.
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