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相关实验视频

Updated: Jul 16, 2025

Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System
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使用单点单光子LiDAR进行无图像目标识别.

Yu Hong, Yuxiao Li, Chen Dai

    Optics express
    |September 15, 2023
    PubMed
    概括

    本研究介绍了一种使用单点单光子光检测和测距 (LiDAR) 的无图像目标识别方法. 这种方法从时间数据中准确地识别了对象类别和姿势,使动态目标的低功耗光学传感成为可能.

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    科学领域:

    • 光子学和光学传感器
    • 人工智能在成像中的使用
    • 机器人技术和自主系统

    背景情况:

    • 单光子光检测和测距 (LiDAR) 为3D成像提供高灵敏度和时间分辨率.
    • 传统的用于目标检测的LiDAR系统通常依赖于通过扫描或基于数组的方法构建图像.
    • 需要有效的,无图像的目标识别技术,特别是对于动态目标.

    研究的目的:

    • 通过使用单点单光子LiDAR系统演示无图像目标识别方法.
    • 利用时间数据和深度学习来实现对象识别,而不需要传统的图像形成.
    • 评估系统在识别目标类和在现实条件下呈现的能力.

    主要方法:

    • 使用单点单光子LiDAR系统,用脉冲激光照明目标.
    • 使用单光子探测器记录了反向散射光子的飞行时间 (ToF).
    • 使用深度学习模型来分析ToF数据以确定目标.

    主要成果:

    • 通过模拟和实验,在识别目标类和姿势方面取得了高准确性.
    • 展示了一种能够识别无人机类型和在户外数百米以上的姿势的紧系统.
    • 验证了无图像,时间数据驱动识别方法的实际可行性.

    结论:

    • 开发的无图像,单点单光子LiDAR方法有效地使用时间数据和神经网络识别目标.
    • 该方法为需要动态目标低功耗光学传感的应用提供了传统成像LiDAR的有希望的替代方案.
    • 该系统在户外环境中的性能凸显了其在无人机检测等领域的现实世界部署潜力.

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