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小目标的实时识别算法用于无人机红外探测.
Qianqian Zhang1,2, Li Zhou1, Junshe An1,3
1National Space Science Center, Chinese Academy of Sciences, Beijing 101499, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究提高了无人驾驶飞行器 (UAV) 对小弱目标的红外探测. 改进的方法提高了实时性能和准确性,这对于有效的空中监视至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 无人驾驶飞行器 (UAV) 红外探测面临着小,弱小的目标和复杂的背景的挑战.
- 现有的目标检测算法难以满足高检测率,低错过检测率和实时性能需求.
- 这些局限性阻碍了有效的空中监视和侦察应用.
研究的目的:
- 为无人机红外图像开发一个改进的小型目标检测方法.
- 提高实时检测性能,降低错误和错过检测率.
- 在复杂的环境中解决弱小目标识别的具体挑战.
主要方法:
- 一个改进的轻量级LCNet网络被集成为功能提取的骨干,以提高实时性能.
- 结合了Squeeze-and-Excitation模块,以改善对弱目标的检测.
- 功能金字塔结构被优化,以进一步完善检测能力.
- 拟议的方法基于Picodet架构.
主要成果:
- 改进的模型显示实时率显著增加,每秒31 (fps).
- 平均平均精度 (MAP) 提高了7%,表明检测精度提高.
- 在HIT-UAV公共数据集上进行了实验验证.
结论:
- 提议的增强检测方法有效地解决了无人机红外小型目标检测现有算法的局限性.
- 实时性能和准确性的改进对于空中监视的实际应用至关重要.
- 这项研究为挑战性红外探测场景提供了强大的解决方案.
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