使用SEB-YOLOv8s实时检测未经授权的无人机
Ao Fang1, Song Feng1, Bo Liang1
1Yunnan Key Laboratory of Computer Technology Application, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究引入了SEB-YOLOv8s,用于实时检测无人机 (UAV),提高准确性和速度,同时最大限度地减少复杂环境中小型目标的内存使用.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于其尺寸和复杂的背景,小型无人机 (UAV) 目标难以实时检测.
- 现有的检测方法经常与高内存和计算成本作斗争,限制了它们的实际应用.
研究的目的:
- 为小型无人机开发一种高效准确的检测方法.
- 为了减少无人机检测系统的内存和计算足迹.
- 在复杂的环境中增强实时检测能力.
主要方法:
- 使用 SPD-Convolution (SPD-Conv) 重建 YOLOv8 网络,以减少计算负载并保留浅层功能.
- 集成AttC2f模块以增强特征提取和信息准确性.
- 应用双级路由注意力优化网络的部,以改善焦点和降低噪音.
主要成果:
- 通过SEB-YOLOv8s方法,在IOU50%的平均精度 (mAP50) 达到90.5%,提高了2.2%.
- 总体准确率达到95.9%,比原始模型增加1.9%.
- mAP50-95改进了2.7%,模型内存大小仅增加了2.5 MB.
结论:
- 拟议的SEB-YOLOv8s方法有效地实现了小型无人机的高精度实时检测.
- 该方法在保持低内存消耗的同时,在检测性能方面取得了显著的改进.
- 这种方法为资源有限的无人机检测应用提供了可行的解决方案.
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