有效的无人机检测使用时间异常和小型时空网络
Abhijit Mahalanobis1, Amadou Tall1
1Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85719, USA.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究介绍了TRX-TCRNet,一个用于红外视频的轻量级无人机检测系统. 它以最小的计算成本实现高精度,非常适合边缘设备.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 在红外 (IR) 图像中检测小型无人机具有挑战性,因为可见度低,分辨率低,背景杂乱,导致高误报和错过检测.
- 现有的方法通常需要大量的计算资源,这限制了它们在实时,基于边缘的场景中的适用性.
研究的目的:
- 开发一个计算效率高,准确的无人机探测系统,用于红外视频序列.
- 解决目前在资源有限的环境中无人机检测技术的局限性.
主要方法:
- 一个轻量级的管道,将一个无监督的统计时间异常探测器 (时间Reed Xiaoli - TRX) 与一个轻量级的卷积神经网络 (TCRNet) 结合起来.
- TRX识别异常,而TCRNet使用时空补丁从杂乱中区分无人机.
- 两个模块的信任地图被添加地融合在一起,用于无人机定位.
主要成果:
- 拟议的TRX-TCRNet实现了卓越的计算效率 (0.17GFLOPs,0.83M参数),在资源使用方面表现优于竞争对手145-795倍.
- 实现了高检测精度,平均平均精度 (mAP50) 为97.40%.
- 证明了显著的效率-性能权衡,适合实时应用.
结论:
- TRX-TCRNet框架提供了前所未有的高检测精度和最低计算要求的平衡.
- 该系统特别适用于资源有限的环境和嵌入式系统,可在边缘设备上有效检测无人机.
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