增强的YOLO11用于基于无人机的轻量级和精确的海上搜索和救援物体检测
Beigeng Zhao1,2, Jiawen Zhao2, Rui Song2
1School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
PloS one
|July 1, 2025
概括
优化的YOLO11通过改进对小型重叠物体的检测来增强基于无人机的海上搜索和救援. 这种先进的物体检测模型在具有挑战性的现实场景中提供了显著的准确性增长.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 海上安全的航行.
背景情况:
- 在无人机图像中检测物体对于海上搜救 (SAR) 非常重要.
- YOLO系列为物体检测提供了速度和准确性的平衡.
- YOLO11看起来很有前途,但需要针对海上SAR进行域特定优化.
研究的目的:
- 增强YOLO11模型,以提高无人机海上SAR的性能.
- 为了应对在SAR图像中检测小,重叠和罕见物体等挑战.
主要方法:
- 在脊柱中集成了一个空间到深度模块.
- 在Neck.中整合了一个内容意识的升级抽样算法.
- 增加了一个额外的检测头,以利用浅图像功能.
主要成果:
- 优化后的YOLO11显著提高了检测小,重叠和罕见物体的性能.
- 在SeaDronesSee数据集上表现优于YOLOv8,YOLO11和MambaYOLO.
- 在轻量化配置中,相对于最先进的方法,实现了20.85%43.70%的相对精度改进.
结论:
- 建议的YOLO11优化对于基于无人机的海上SAR非常有效.
- 这种增强的模型为海上救援行动中对象检测提供了更准确,更有效的解决方案.
- 优化的模型提供了显著的性能提升,特别是在轻量化配置.
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