DRBD-YOLOv8:一个轻量级和高效的反无人机检测模型
Panpan Jiang1,2,3, Xiaohua Yang2,4, Yaping Wan2,4
1School of Nuclear Science and Technology, University of South China, Hengyang 421001, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
一个新的轻量级深度学习模型DRBD-YOLOv8增强了反无人机探测能力. 它实现了边缘设备的高精度和速度,克服了当前系统的局限性.
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:
- 越来越多的安全和隐私问题需要有效的反无人飞行器 (UAV) 系统.
- 现有的深度学习模型难以平衡基于边缘的反UAV检测的准确性,速度和资源效率.
研究的目的:
- 开发一种轻量级和高效的反无人机检测模型,用于边缘设备上的实时应用.
- 为了提高检测精度,处理速度和模型大小之间的平衡.
主要方法:
- 拟议的DRBD-YOLOv8模型包含重新参数化的跨阶段高效分层注意网络 (RCELAN) 和双向特征金字塔网络 (BiFPN).
- 引入了一种新的DN-ShapeIoU损失函数,以提高检测准确度.
- 利用深度可分离的卷积来减少计算复杂性.
主要成果:
- 在mAP50,mAP95,精度和FPS方面,DRBD-YOLOv8表现出比YOLOv8n更好的性能.
- 该模型实现了显著减少的GFLOP和参数数量,文件大小为3.25M.
- 与YOLOv8n.相比,模型大小减少了近50%.
结论:
- DRBD-YOLOv8提供了一个令人信服的解决方案,用于在资源受限的边缘设备上实时检测反UAV.
- 该模型的轻量级设计,高精度和快速处理速度使其适合于实际的安全应用.
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