从图像/视频中检测和分类车辆的深度学习技术:一项调查
Michael Abebe Berwo1, Asad Khan2, Yong Fang1
1School of Information and Engineering, Chang'an University, Xi'an 710064, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
深度学习模型为智能交通系统提供高效的车辆检测和分类. 本调查分析了深度学习技术,数据集和应用程序,突出了实时交通管理的进步和挑战.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 智能运输系统 (ITS) 是一种智能运输系统.
背景情况:
- 从图像/视频中检测和分类车辆对于ITS至关重要,但由于外观变化而具有挑战性.
- 深度学习 (DL) 的进步正在推动各种领域对强大的计算机视觉解决方案的需求.
研究的目的:
- 调查和分析用于车辆检测和分类的各种深度学习方法.
- 调查这些DL技术在ITS中的应用,包括交通密度估计和收费管理.
- 提供DL技术的详细概述,基准数据集和现场挑战.
主要方法:
- 关于对象检测和分类的深度学习架构的综合文献综述.
- 分析常用于车辆检测和分类任务的基准数据集.
- 调查与现实世界ITS应用相关的性能指标和挑战.
主要成果:
- 深度学习模型显示,车辆检测和分类的效率和稳定性得到了显著提高.
- 不同的DL架构对各种ITS应用有效,从实时流量监控到自动化收费管理.
- 关键的挑战包括处理外观变化,遮和实现实时性能.
结论:
- 深度学习是一个强大的工具,用于在智能交通系统中推进车辆检测和分类.
- 对DL技术,数据集和应对当前挑战的持续研究将进一步增强ITS能力.
- 该论文为智能交通和计算机视觉领域的研究人员和从业人员提供了宝贵的资源.
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