根据轨迹运动和深度学习,区分无人机与鸟类.
Andrii Nesteruk1,2, Valerii Nikitin1,2, Yosyp Albrekht1
1Faculty of Informatics and Computer Engineering, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 37 Peremohy Ave., 03056 Kyiv, Ukraine.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
从远距离区分无人机和鸟类对于空域安全至关重要. 这项研究使用了深度学习模型的运动轨迹分析,可靠地将无人驾驶飞行器 (UAV) 与鸟类区分开来,即使它们只有少数像素.
科学领域:
- 航空航天工程 航空航天工程
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 无人驾驶飞行器 (UAV) 和鸟类共享低空空域,需要可靠的安全识别.
- 传统的基于外观的识别在远程场景中失败,目标很小,缺乏细节.
研究的目的:
- 开发一种可靠的方法来区分无人机和鸟类在远程空中监视.
- 利用运动轨迹分析作为基于外观检测的补充提示.
主要方法:
- 一个以模型驱动的模拟管道生成了多飞机,固定翼无人机和鸟类轨迹的合成数据.
- 一个双向长短期记忆 (LSTM) 网络被训练在时间序列图像平面坐标和表面大小上来分类运动模式.
- 该模型分析了轨迹特征,如平滑度,转向行为和速度波动.
主要成果:
- 在LSTM网络成功地对合成数据进行了无人机类和鸟类类运动模式的分类.
- 仅靠运动轨迹的线索就能够可靠地将无人机与鸟类分开,即使视觉细节很少.
- 该方法证明了对现实世界监控系统的概括能力.
结论:
- 运动轨迹分析是在具有挑战性的远程场景中早期区分无人机和鸟类的有效方法.
- 拟议的模拟和分类管道为基于轨迹的识别器提供了可重复的测试平台.
- 这种方法为综合监控解决方案提供了一个补充和强大的信号,独立于基于外观的限制.
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