Vehicle Brand Detection Using Deep Learning Algorithms

Mehmet Furkan Kunduraci, Humar Kahramanlı Örnek
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引用次数: 2

Abstract

Today, information technologies are used in almost every stage of life. It seeks to find solutions too many issues and problems. Image processing applications have been widely used in many areas in recent years and are trying to solve problems. Many applications which perform tasks such as classification, counting, measurement, target tracking have been developed. The aim of this study is to provide a solution for different applications using an effective and cost-effective method to detect the brand and model of vehicles. A classification method is implemented using deep neural network in the determination of the vehicle brand. The proposed solution is tested on various images taken from different angles and obtained from different sources. Faster-RCNN method which is one of deep neural networks is used to brand detection of vehicles in this study. It is observed that Faster-RCNN method performs 67.66% classification accuracy.
基于深度学习算法的汽车品牌检测
今天,信息技术几乎应用于生活的每个阶段。它试图找到太多问题和问题的解决方案。近年来,图像处理在许多领域得到了广泛的应用,并正在努力解决问题。许多执行分类、计数、测量、目标跟踪等任务的应用程序已经开发出来。本研究的目的是为不同的应用提供一个解决方案,使用一种有效和经济的方法来检测车辆的品牌和型号。提出了一种基于深度神经网络的汽车品牌分类方法。在不同角度和不同来源的不同图像上对所提出的解决方案进行了测试。本文采用深度神经网络中的Faster-RCNN方法进行车辆品牌检测。观察到fast - rcnn方法的分类准确率为67.66%。
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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