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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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对ADAS的对象检测,识别和跟踪算法-关于最近趋势的研究

Vinay Malligere Shivanna1, Jiun-In Guo1,2,3

  • 1Department of Electrical Engineering, Institute of Electronics, National Yang-Ming Chiao Tung University, Hsinchu City 30010, Taiwan.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
概括

先进的驾驶辅助系统 (ADAS) 使用对象检测,识别和跟踪算法来提高车辆的安全性. 本综述涵盖了最先进的ADAS算法,并讨论了挑战性驾驶条件的未来研究需求.

关键词:
先进的驾驶辅助系统 (ADAS)深度学习是一种深度学习.对象检测检测对象检测对象检测对象跟踪是指对象的跟踪.

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科学领域:

  • 汽车工程 汽车工程
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 先进的驾驶辅助系统 (ADAS) 越来越多地集成到车辆中,以提高安全性和驾驶体验.
  • ADAS使用像摄像头,雷达和激光雷达这样的传感器来感知环境.

研究的目的:

  • 审查ADAS中最先进的对象检测,识别和跟踪算法.
  • 探索最近的趋势,数据集和ADAS算法的功能.

主要方法:

  • 对著名的ADAS算法进行了全面的文献审查.
  • 对象检测,识别和跟踪技术的分析.
  • 检查在ADAS研究中使用的数据集.

主要成果:

  • 目前用于ADAS功能的对象检测,识别和跟踪算法的概述.
  • 识别最近的趋势和常用的数据集.
  • 讨论这些算法在提高车辆安全性和性能方面的作用.

结论:

  • 对象检测,识别和跟踪对于ADAS至关重要.
  • 未来的研究应该专注于改进用于具有挑战性的环境的算法,如低可见度和高交通密度等.