基于CNN机器学习算法的实时交通标志识别关键研究

Megha Vamsi Kiran Choda, Sri Vardhan Perla, Brahmender Shaik, Yuva Teja Anirudh Yelchuru, P. Yalla
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引用次数: 2

摘要

实时交通标志识别系统(RTTSRS)用于识别交通标志(左转、右转、限速60公里/小时等),在无人驾驶等领域起着至关重要的作用。利用实时交通标志识别技术,可以减少交通相关问题。它分为两种类型:定位和识别。定位处理的是识别和定位半径内的交通标志区域。实时交通标志识别用于在提供的空间(矩形)内识别交通标志区域。本研究描述了一种交通标志识别系统的方法,许多机器学习算法,如支持向量机(SVM)和卷积神经网络(CNN)已经被研究用于识别交通标志。本研究对各种机器学习算法进行了重要的研究,这些算法可以高精度地预测、识别实时交通标志。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Critical Survey on Real-Time Traffic Sign Recognition by using CNN Machine Learning Algorithm
Real-Time Traffic Sign Recognition System (RTTSRS) is used for recognizing the traffic signboards (Take left, take right, speed limit 60 kmph… etc.), it plays a crucial role in the domains of driverless vehicles etc. By using Real-Time Traffic Sign Recognition, Traffic related problems can be reduced. It is categorized into two types- localization and recognition. Localization deals with identifying and locating traffic sign regions within the radius. Real-Time Traffic Sign Recognition is used to identify the traffic sign region within the space (rectangular) provided. This study describes an approach for a traffic sign recognition system, many machine learning algorithms like Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) have been studied for recognizing traffic signs. This study has conducted a critical investigation on various machine learning algorithms which gives high accuracy to predict, recognize real-time traffic signs.
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