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相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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相关实验视频

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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在自动驾驶中探索LiDAR语义细分的对抗性稳定性

K T Yasas Mahima1, Asanka Perera2, Sreenatha Anavatti1

  • 1School of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, Australia.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括

本研究探讨了对自动驾驶汽车3D LiDAR语义细分的对抗性攻击. 调查结果显示,地面水平的点是脆弱的,影响知觉系统的稳定性.

关键词:
李达尔 (LiDAR) 是一种激光雷达.敌对的攻击是对抗性的攻击.自动驾驶汽车是自动驾驶的语义细分 语义细分 语义细分 语义细分

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 深度学习在2D/3D视觉方面表现出色,但容易受到对抗性攻击.
  • 越来越多地研究了对自动驾驶汽车感知的对抗性攻击,但3D LiDAR语义细分仍未得到充分探索.

研究的目的:

  • 调查自动驾驶汽车中3D LiDAR语义细分的对抗性稳定性.
  • 开发和分析基于点的LiDAR对抗性攻击方法.

主要方法:

  • 开发和分析了三种基于LiDAR点的对抗性攻击方法.
  • 通过使用SemanticKITTI数据集对各种网络进行攻击评估.
  • 研究了类智能点分布对对抗性强度的影响.

主要成果:

  • 圆柱3D网络对分析的对抗性攻击表现出最高的易感性.
  • 发现地面点特别容易受到点扰动攻击.
  • 使用点数据表示的网络对攻击可转移性表现出了显著的抵抗力.

结论:

  • 敌对攻击对自动驾驶汽车中的3D LiDAR语义细分构成重大威胁.
  • 了解类智能漏洞,特别是对于基层点,对于开发强大的系统至关重要.
  • 结果为创建先进的对抗性攻击和有效的对抗措施为基于LiDAR的感知提供了基础.