在多场景感知上准确识别无人机,使用YOLOv9-CAG
Jincan Zhu1,2, Jian Rong3,4, Weili Kou1,2
1College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224, China.
Scientific reports
|July 30, 2025
概括
这项研究介绍了YOLOv9-CAG,一种新的多式无人机探测系统. 它通过使用可见光,红外和音频传感器在具有挑战性的条件下显著提高了无人机识别精度.
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:
- 无人驾驶飞行器 (UAV) 的广泛使用需要强有力的安全措施.
- 目前的无人机识别方法与远距离,复杂和夜间场景作斗争.
- 准确可靠的无人机检测对于航空安全和保障至关重要.
研究的目的:
- 为增强无人机识别开发一种先进的多模式算法.
- 在具有挑战性的环境条件下解决现有方法的局限性.
- 提高无人机检测系统的准确性和可靠性.
主要方法:
- 拟议的YOLOv9-CAG算法集成可见光,红外和音频传感器数据.
- 集成的CAM上下文功能增强模块用于小目标功能提取.
- 集成的GAM注意力机制和增强的AKConv动态卷积,以改善焦点和细节捕捉.
主要成果:
- 在可见光数据集上,YOLOv9-CAG实现了92.0%的mAP0.50,比YOLOv9.9有10.8%的改进.
- 在红外数据集上,mAP0.50和召回分别达到86.5%和89.2% (12.4%和11.4%的增长).
- 在mAP0.50和音频数据集的回忆以及有效的鸟类识别方面取得了显著的改进.
结论:
- YOLOv9-CAG模型在多场景无人机识别方面表现出色.
- 这种多式联运框架的先驱者在具有挑战性的条件下提高了识别准确性.
- 该研究推进了无人机识别技术,提高了安全和安全能力.
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