用于反无人机检测的空间时间特征融合:整合跨动态和外观
Yake Zhang1, Xiaoxi Fu1, Yunfeng Zhou1
1College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了MSM-YOLO,这是通过结合静态和动态检测来检测复杂环境中的小型无人机 (UAV) 目标的改进方法. 它显著提高了低缓小无人机的检测准确度和召回.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 在混乱的环境中检测小型,缓慢移动的无人机 (UAV) 存在重大挑战.
- 现有的方法往往在低可见度,复杂的背景和微妙的运动检测方面扎.
研究的目的:
- 开发一个先进的检测系统,用于在复杂场景中低缓小无人机目标.
- 为了提高无人机检测的精度,回忆和平均平均精度 (mAP).
- 创建一个可在嵌入式硬件上部署的实用和高效的系统.
主要方法:
- 一个改进的YOLOv11静态探测器,包括SPD Conv,BiFPN和高分辨率检测头部.
- 一个动态的目标检测算法来捕捉微妙的运动特征.
- 一个综合战略,融合静态和动态检测判断.
主要成果:
- 拟议的MSM-YOLO方法实现了94%的精度,92%的回忆率和86.3%的mAP50精度.
- 与基线YOLOv11检测器相比,显著改善,精度增加12.1%,回忆增加29.5%,mAP50.6增加29.6%.
- 废弃性研究证实了单个模块的有效性.
- 在RK3588嵌入式系统上的优化部署实现了每秒100 (fps).
结论:
- 新的时空信息融合方法有效地提高了在复杂的背景中检测小型无人机的性能.
- MSM-YOLO在现实世界空对空无人机检测应用中展示了卓越的性能和实用性.
- 该系统的效率和准确性使其适合在资源有限的嵌入式平台上部署.
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