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交通安全的自动道路缺陷和异常检测:系统审查

Munish Rathee¹, Boris Bačić¹, Maryam Doborjeh^1,2

¹School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand.

Sensors (Basel, Switzerland)

|July 8, 2023

概括

本系统性审查全面分析了自动道路缺陷和异常检测 (ARDAD) 的计算机视觉应用程序. 它确定了研究差距和趋势,以利用先进的传感器技术提高交通安全.

科学领域:

工程工程师工程师工程师
计算机科学计算机科学
运输科学运输科学

背景情况:

与交通有关的伤害和死亡需要先进的安全解决方案.
计算机视觉 (CV) 和传感器技术为减轻道路危险提供了潜力.
现有的审查缺乏对自动道路缺陷和异常检测 (ARDAD) 的CV进行全面调查.

研究的目的:

系统地审查和介绍ARDAD的CV应用程序的最新情况.
确定该领域的研究差距,挑战和未来影响.
在ARDAD中整合流行的开放访问数据集和技术趋势.

主要方法:

从2000年到2023年,对116篇论文进行了系统的文献综述.
主要数据来源:斯科普斯和Litmaps.
分析研究趋势,数据集和报告的表现.

主要成果:

确定了ARDAD.AD 中的主要研究缺口和挑战.
为ARDAD研究编目了18个受欢迎的开放访问数据集.
突出技术趋势加速传感器技术在ARDAD中的应用.

结论:

这篇评论提供了ARDAD中CV的全面概述.
鉴定到的文物将有助于研究人员推进交通安全解决方案.
未来的研究应该专注于解决发现的差距,以改善道路安全.

关键词:

阿尔达达德阿尔达德是什么意思计算机视觉计算机视觉深度学习是一种深度学习.机器学习是机器学习.汽车驾驶者的安全安全.在道路上的异常检测检测.结构损坏检测结构损坏检测转移学习转移学习

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