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通过强度染和3D点插值技术提高基于深度学习的细分精度,以减轻传感器变量.

Myeong-Jun Kim1, Suyeon Kim2, Banghyon Lee2

  • 1Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea.

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概括
此摘要是机器生成的。

本研究引入了LiDAR传感器数据的新方法,以改善自动驾驶汽车中的物体检测. 这些技术通过减少培训与现实数据之间的差异来提高性能,从而提高了准确度约20%.

关键词:
3D细分是指三维的细分.这是一个激光雷达传感器.数据注释数据注释深度学习是一种深度学习.强度染 染 强度染对象检测检测对象检测对象检测

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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
  • 传感器融合式传感器

背景情况:

  • 深度学习细分网络对于使用LiDAR的自动驾驶汽车对象识别至关重要.
  • 训练和现实世界部署之间的LiDAR传感器数据 (坐标,强度) 的差异会导致性能下降.

研究的目的:

  • 开发和评估用于协调LiDAR传感器数据属性的新技术.
  • 提高自动驾驶汽车深度学习感知系统的性能和可靠性.
  • 为了防止在训练和部署时使用各种LiDAR传感器时的性能下降.

主要方法:

  • 拟议的新型强度染技术.
  • 开发了数据插值方法来协调传感器数据差异.
  • 在现实世界自动驾驶场景中对物体跟踪的评估方法.

主要成果:

  • 实现了大约20%的平均交叉与欧盟 (mIoU) 业绩的改善.
  • 证明了传感器数据属性的有效协调.
  • 在不同类型的传感器中验证了性能降低的预防.

结论:

  • 拟议的强度染和数据插值技术显著改善了基于LiDAR的自动驾驶汽车对象检测.
  • 这些方法可以使用多样化的开源数据集,节省数据注释的时间和资源.
  • 这种方法提高了自动驾驶感知系统的稳定性和可靠性.