立即材料分类使用一个极化多变的RMCW LIDAR.
Cibby Pulikkaseril1, Duncan Ross1, Alexander Tofini1
1Baraja Pty Ltd., Suite 303, Building 1, 3 Richardson Pl., North Ryde, NSW 2113, Australia.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
多极化光检测和射程 (LIDAR) 传感器捕获目标极度测量数据. 这使得使用训练有素的神经网络能够快速进行材料分类,对于常见材料,准确度为85.4%.
科学领域:
- 光子学和材料科学 材料科学
- 光学传感技术的技术
背景情况:
- 光检测和测距 (LIDAR) 传感器可以获取极度测量信息.
- 多极化接收器增强了LIDAR用于目标表征的能力.
研究的目的:
- 为了展示一个光子接收器用于实时极度测量激光雷达数据采集.
- 通过神经网络利用这些数据进行材料分类.
主要方法:
- 开发一种光子接收器架构,用于极化多变的LIDAR.
- 在每次射击的基础上对极度测量数据的捕获和分析.
- 训练一个神经网络用捕获的极度测量数据进行材料分类.
主要成果:
- 立即偏振分析集成到LIDAR点云中.
- 实现了塑料,木材,混凝土和涂层的85.4%的分类准确度.
- 证明了极度测量激光雷达 (Polarimetric LIDAR) 在材料识别方面的有效性.
结论:
- 光子接收器可以实现先进的极度测量激光雷达功能.
- 极度测量激光雷达与神经网络相结合,为材料分类提供了一个强大的工具.
- 这项技术在遥感和材料识别方面有潜在的应用.
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