Enabling Vehicle Search Through Robust Licence Plate Detection

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2023-08-03 DOI:10.1109/icABCD59051.2023.10220508

Alden Boby, Dane Brown, James Connan, Marc Marais, Luxulo Lethukuthula Kuhlane

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引用次数: 0

Abstract

Licence plate recognition has many practical applications for security and surveillance. This paper presents a robust licence plate detection system that uses string-matching algorithms to identify a vehicle in data. Object detection models have had limited application in the character recognition domain. The system utilises the YOLO object detection model to perform character recognition to ensure more accurate character predictions. The model incorporates super-resolution techniques to enhance the quality of licence plate images to increase character recognition accuracy. The proposed system can accurately detect license plates in diverse conditions and can handle license plates with varying fonts and backgrounds. The system's effectiveness is demonstrated through experimentation on components of the system, showing promising license plate detection and character recognition accuracy. The overall system works with all the components to track vehicles by matching a target string with detected licence plates in a scene. The system has potential applications in law enforcement, traffic management, and parking systems and can significantly advance surveillance and security through automation.

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通过稳健的车牌检测实现车辆搜索

车牌识别在安全和监控方面有许多实际应用。本文提出了一种鲁棒车牌检测系统，该系统使用字符串匹配算法来识别数据中的车辆。目标检测模型在字符识别领域的应用有限。该系统利用YOLO对象检测模型进行字符识别，以确保更准确的字符预测。该模型采用超分辨率技术来提高车牌图像的质量，以提高字符识别的准确性。该系统能够在不同条件下准确检测车牌，并能处理不同字体和背景的车牌。通过对系统各组成部分的实验，验证了系统的有效性，显示出良好的车牌检测和字符识别精度。整个系统与所有组件一起工作，通过将目标字符串与场景中检测到的车牌进行匹配来跟踪车辆。该系统在执法、交通管理和停车系统中具有潜在的应用前景，并可以通过自动化显著提高监控和安全水平。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.