使用维康系统注册的多式联机数据进行YOLOv5无人机检测
Wojciech Lindenheim-Locher1, Adam Świtoński2,1, Tomasz Krzeszowski3,1
1Polish-Japanese Academy of Information Technology, ul. Koszykowa 86, 02-008 Warsaw, Poland.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究精确地使用YOLOv5在多摄像头图像上检测无人机,改进了3D无人机跟踪. 一个新的评估指标 - - 平均心点距离 - - 增强了检测性能分析.
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
- 人工智能的人工智能
背景情况:
- 准确的3D无人机跟踪对于监视和自主导航等应用至关重要.
- 现有的检测方法在复杂的多摄像头环境中往往难以准确.
研究的目的:
- 为3D无人机跟踪挑战的初步阶段开发和评估精确的无人机检测方法.
- 为评估3D无人机检测性能引入一种新的评估指标.
主要方法:
- 培训和测试YOLOv5深度学习网络的真实多式联络数据,包括同步的视频和运动捕捉数据.
- 使用带有标记的不对称十字来准确地确定3D位置和方向.
- 整合来自AirSim模拟平台的合成数据,进行可靠的培训和测试.
主要成果:
- 证明了YOLOv5在同步多摄像头系统中用于无人机检测的有效性.
- 提出并验证了一种新的度量 (中心体之间的平均距离) 用于评估3D检测准确度.
- 通过基于标记器的交叉检测,在无人机定位方面取得了有希望的结果.
结论:
- YOLOv5网络显示了在3D跟踪场景中精确探测无人机的巨大潜力.
- 拟议的评估指标为3D检测性能提供了更充分的评估.
- 结合真实和模拟数据,提高了无人机检测模型的稳定性.
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