无人驾驶飞行器实时检测传感器使用改进的YOLOv8s算法
Fuhao Lu1, Chao Zeng1, Hangkun Shi1
1School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究通过将长短期记忆网络与YOLOv8s集成来增强无人机检测,改善了对空域安全的未经授权的无人机 (UAV) 的实时跟踪.
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:
- 无人机 (UAV) 定位对于低空经济应用至关重要.
- 传统的YOLOv8s算法因单限制而难以进行错过的检测.
- 未经授权的无人机活动或"黑飞行"给空域管理带来了重大挑战.
研究的目的:
- 为了提高未经授权的无人机的准确性和实时检测.
- 克服单功能依赖现有算法的局限性.
- 为了增强无人机轨迹预测和跟踪能力.
主要方法:
- 长期短期记忆 (LSTM) 网络与YOLOv8s框架的整合.
- 利用时间序列建模用于历史特征保留和动态轨迹预测.
- 使用一个结合损失函数 (边界盒回归和二进制交叉) 优化与亚当算法.
- 使用蒙特卡洛随机抽样验证训练数据分布,以提高概括性.
主要成果:
- 与传统方法相比,无人机检测性能显著提高.
- 在检测未经授权的无人机方面,即使在复杂的场景中,也表现出了强大的稳定性.
- 在RK3588嵌入式系统上部署时,实现了低错误负率.
结论:
- 拟议的LSTM增强的YOLOv8s算法提供了卓越的无人机检测和跟踪.
- 该方法在空域管理和反无人机作战方面具有很强的实际应用潜力.
- 实时性能和准确性使其适用于关键安全应用.
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