YOLO11s-UAV:在无人机空中图像中检测小物体的先进算法
Qi Mi1,2,3,4, Jianshu Chao2,3,4, Anqi Chen1,2,3,4
1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China.
Journal of imaging
|February 26, 2026
概括
这项研究介绍了YOLO11s-UAV,这是一个改进的算法,用于从无人机 (UAV) 探测空中图像中的小物体. 改进后的模型显著提高了检测准确度,同时降低了实时应用的计算负载.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 无人驾驶飞行器 (UAV) 在农业和搜救等应用中变得越来越重要.
- 在空中图像中检测小物体存在挑战,因为像素稀疏,背景复杂,机载计算能力有限.
- 现有的物体检测算法难以满足空中监视的独特需求.
研究的目的:
- 开发基于无人机的改进小物体检测算法,以提高空中成像的性能.
- 为了应对小型物体检测,复杂的背景和无人机应用中的计算限制的挑战.
- 在无人机平台上创建一个更有效,更准确的实时物体检测模型.
主要方法:
- 引入了内容意识重组和交互特征金字塔网络 (CARIFPN),用于优越的小物体特征检测和降低网络复杂性.
- 在骨干中实现空间到深度扩展智能剩余卷积 (S2DResConv),以防止信息丢失和捕获多层次的环境.
- 集成的灵活SimAM (FlexSimAM),一个无参数的注意力模块,以高效地增强复杂的空中场景中小物体的特征.
主要成果:
- 与YOLO11s基线相比,VisDrone-DET2019验证集 (46.0%) 的mAP@0.5得到了7.8%的改善,测试集的5.9% (37.3%) 的增加.
- 模型参数减少了55.3%,并对TinyPerson (7.2%) 和UAVDT-DET (3.0%) 数据集进行了显著改进.
- 在NVIDIA Jetson Orin NX SUPER上实现了33 FPS,证实了实时内置处理可行性,处理时间减少了21.4%.
结论:
- 拟议的YOLO11s-UAV算法有效地提高了空中图像中的小物体检测.
- 新的CARIFPN,S2DResConv和FlexSimAM模块有助于提高准确性和效率.
- 该模型的性能和复杂性降低使其适用于实时机载无人机应用.
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