基于连续合神经网络的红外无人机目标检测
Zhuoran Yang1, Jing Lian2, Jizhao Liu1
1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Micromachines
|November 25, 2023
概括
这项研究引入了一个由大脑启发的框架,用于在红外图像中检测无人机 (UAV). 该方法在具有挑战性的条件下提高了检测精度,超过了现有的技术.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 红外成像技术 红外成像技术
背景情况:
- 无人驾驶飞行器 (UAV) 的检测对于安全至关重要.
- 红外成像在复杂的环境中提供了优势,但面临着诸如低信号对杂乱比率和有限数据等挑战.
- 由于图像噪音和杂乱,传统方法在红外无人机检测方面遇到了困难.
研究的目的:
- 开发一个有效的框架,用于在红外图像中检测无人机.
- 解决现有方法在处理杂和混乱的红外数据方面的局限性.
- 为了利用大脑启发的机制来改善无人机检测.
主要方法:
- 一个新的框架,灵感来自于人类的视觉处理,用于无人机检测.
- 使用连续合神经网络 (CCNN),其参数由图像统计确定.
- 采用代的像素分组,通过扩张/侵蚀进行细分,并为最终检测采用最小的环形矩形.
主要成果:
- 与最先进的脑启发方法相比,拟议的框架实现了更高的性能.
- 在无人机红外图像上显示了74.79%的高平均交集度 (IoU),最高为97.01%.
- 有效地克服了在红外无人机探测中低信号与杂乱和信号与噪声比率的挑战.
结论:
- 灵感来自大脑的框架为红外无人机检测提供了有效的解决方案.
- 该方法在检测准确性和稳定性方面取得了显著的改进.
- 通过先进的空中监视技术,提供了一种增强安全的有希望的方法.
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