小目标检测算法基于改进的YOLOv11nn
Ke Zeng1, Wangsheng Yu2, Xianxiang Qin2
1Graduate School, Air Force Engineering University, Xi'an 710051, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究介绍了一种增强的YOLOv11n算法,用于在无人机图像中检测小目标. 改进的模型显著提高了复杂背景的检测准确性,超过了基准.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 在无人机空中摄影中检测小目标是具有挑战性的,因为规模和背景的复杂性.
- 现有的算法在下采样过程中经常与功能丢失和交叉级别冲突作斗争.
研究的目的:
- 提出一个改进的YOLOv11n算法,用于在无人机空中摄影中增强小目标检测.
- 解决特征融合,特征突出显示和检测准确性的局限性.
主要方法:
- 在160x160特征层上实现了一个检测头,并使用非对称特征金字塔网络 (AFPN) 融合了特征.
- 集成空间通道注意力SPPF (SCASPPF),并增强了MPDIoU和InnerIoU的损失功能.
- 利用初始深度卷积 (IDC) 来改进C3k2模块,以扩大受体场.
主要成果:
- 改进的YOLOv11n算法在Visdrone2019数据集上实现了39.256%的mAP@0.5.
- 这与基准YOLOv11n的32.567%mAP@0.5.5.n相比,代表了6.689%的改善.
- 这些修改有效地减轻了特征损失,并提高了模型检测小物体的能力.
结论:
- 拟议的改进显著提高了无人机空中图像中小目标检测性能.
- 整合AFPN,SCASPPF,增强损失功能和IDC有助于提高检测准确度.
- 这种算法为复杂的空中监视和监控任务提供了更强大的解决方案.
关键词:
AFPN AFPN AFPN 的意思是这是一个IDC IDC.内在的时间 内部时间MPDIoUU 的意思是在SPPF中,SPPF是SPPF.这是YOLOv11n.无人机 无人机 无人机小目标检测检测小目标检测更多相关视频
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