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A new comparison framework to survey neural networks-based vehicle detection and classification approaches
International Journal of Communication Systems ( IF 2.1 ) Pub Date : 2021-07-27 , DOI: 10.1002/dac.4928
Sajjad Hashemi 1 , Hojjat Emami 1, 2 , Amin Babazadeh Sangar 1
Affiliation  

The vehicle detection and classification (VDC) problem has received much attention recently due to the increased security threats and the need to develop intelligent transportation systems. A large number of approaches have been proposed for the VDC problem using neural networks. To determine how neural networks-based approaches have developed for the VDC in recent years, this paper surveys the VDC approaches through a literature review with the range Jan. 2012 through Apr. 2021. To do this, we introduce a new comparison framework to classify and compare the VDC approaches. Our proposed framework is composed of nine comparison dimensions: input data type, vehicle type, scale, scope, dynamicity, vehicle detection method, vehicle classification method, application, and evaluation method. Next, using the proposed framework, we discuss the evolution of the VDC approaches and identify several open issues that have emerged in the field. This paper provides a guide for researchers to use or design robust VDC systems with proper characteristics based on their needs.

中文翻译:

一种新的比较框架,用于调查基于神经网络的车辆检测和分类方法

由于安全威胁的增加和开发智能交通系统的需要,车辆检测和分类(VDC)问题最近受到了广泛关注。已经提出了大量使用神经网络解决 VDC 问题的方法。为了确定近年来基于神经网络的方法是如何为 VDC 开发的,本文通过 2012 年 1 月至 2021 年 4 月范围内的文献综述调查了 VDC 方法。为此,我们引入了一个新的比较框架来分类并比较 VDC 方法。我们提出的框架由九个比较维度组成:输入数据类型、车辆类型、规模、范围、动态性、车辆检测方法、车辆分类方法、应用程序和评估方法。接下来,使用建议的框架,我们讨论了 VDC 方法的演变,并确定了该领域出现的几个未解决的问题。本文为研究人员根据他们的需要使用或设计具有适当特性的稳健 VDC 系统提供了指导。
更新日期:2021-08-16
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