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图像处理算法分析用于路边野生动物检测

Mindaugas Knyva1, Darius Gailius2, Šarūnas Kilius1

  • 1Department of Electronics Engineering, Kaunas University of Technology, Studentu Str. 50-457, 51368 Kaunas, Lithuania.

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概括

一种运动检测方法提供了使用热成像的路边野生动物检测的最佳解决方案,实现高精度和灵敏度,以防止嵌入式系统中的野生动物与车辆碰撞.

关键词:
嵌入式系统 嵌入式系统图像处理算法图像处理算法运动检测,运动检测检测.路边监控是道路上的监控.热成像是一种热成像技术.野生动物检测检测器

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 野生动物管理 野生动物管理

背景情况:

  • 野生动物与车辆的碰撞对动物和人类都有很大的风险.
  • 有效的检测系统对于减轻这些碰撞至关重要.
  • 嵌入式系统为实时路边监控提供了可行的解决方案.

研究的目的:

  • 对路边野生动物检测的五种图像处理技术进行比较分析.
  • 确定在嵌入式系统中实现的最佳方法.
  • 通过减少野生动物与车辆碰撞,提高道路安全.

主要方法:

  • 用SIFT评估双边过,用Canny边缘检测进行高斯过,颜色定量化,运动检测和YOLOv8n神经网络.
  • 应用算法从定制路边监控系统的热图像.
  • 根据执行时间,灵敏度,特异性和准确性来评估性能.

主要成果:

  • 运动检测产生了最高的灵敏度 (92.31%) 和精度 (87.50%).
  • 双边过提供了最快的执行时间 (0.093秒).
  • 巧妙的边缘检测提供了高特异性 (90.00%).

结论:

  • 运动检测是可靠的路边动物检测的首选方法,因为它的高灵敏度和准确性.
  • 选择的方法在各种数据集中显示出稳定性,尽管性能可能有所不同.
  • 建议进一步开发,将其集成到嵌入式系统中,以减轻碰撞.