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相关实验视频

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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SMIFormer:通过多视图交互式变压器从4D成像雷达学习空间特征表示,用于从4D成像雷达检测3D物体.

Weigang Shi1, Ziming Zhu2, Kezhi Zhang2

  • 1School of Automotive Studies, Tongji University, Shanghai 201804, China.

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

本研究介绍了SMIFormer,这是一种用于4D成像雷达的新型网络,通过融合多视图数据 (鸟眼,正面,侧面) 来增强自动驾驶,以克服点云稀疏性和噪声,以改进3D对象检测.

关键词:
3D对象检测检测 3D对象检测4D成像雷达是指4D成像雷达.自动驾驶自动驾驶的自动驾驶.深度学习是一种深度学习.多视图功能互动互动.一个点云,一个点云.沃克塞尔的功能是解.

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科学领域:

  • 机器人技术和自主系统
  • 计算机视觉 计算机视觉
  • 传感器融合式传感器

背景情况:

  • 4D毫米波 (mmWave) 成像雷达为自动驾驶提供了具有成本效益和耐天气的传感.
  • 挑战包括稀疏和杂的点云,限制实际应用.
  • 现有的方法在单视图数据中缺乏足够的特征表示方面扎.

研究的目的:

  • 推出SMIFormer,一个用于4D雷达的多视图功能融合网络.
  • 为了解决4D雷达数据中稀疏和杂的点云的局限性.
  • 为了提高自动驾驶系统中的3D物体检测性能.

主要方法:

  • 开发了SMIFormer,这是单模4D雷达输入的网络框架.
  • 将3D场景解成鸟视图 (BEV),前视图 (FV) 和侧视图 (SV).
  • 拟议的多视图功能交互 (MVI) 用于内视图和交叉视图功能集成.

主要成果:

  • 在View-of-Delft (VoD) 数据集上评估的SMIFormer.
  • 在完全注释的区域实现了48.77%的平均精度 (mAP).
  • 在驾驶走廊区域达到71.13%的mAP.

结论:

  • 通过集成多视图雷达数据,SMIFormer有效地建模了3D场景.
  • 多视图方法克服了由稀疏点云引起的单视图限制.
  • 4D雷达显示了在自动驾驶中推进3D物体检测的巨大潜力.