CF-YOLO用于基于YOLOv11算法在无人机图像中检测小目标
Chengcheng Wang1,2,3, Yuqi Han1,2, Chenggui Yang1,2
1School of Information, Yunnan Normal University, Kunming, 650500, China.
Scientific reports
|May 14, 2025
概括
这项研究介绍了CF-YOLO,一种基于无人机的小目标检测算法. 通过改善特征融合和信息保留,CF-YOLO显著提高了遥感图像中小物体的检测精度.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 无人机图像对目标检测提出了挑战,原因是尺度变化和小,细节较低的物体.
- 现有的算法在小型目标的多尺度特征融合中扎着信息丢失和特征冗余.
研究的目的:
- 开发一种改进的小型目标检测算法,用于基于无人机的遥感.
- 为了解决信息丢失和特征冗余问题在多尺度特征融合.
主要方法:
- 提出了一个跨度特征金字塔网络 (CS-FPN),以减轻层次卷积结构中的信息丢失.
- 引入了特征重新校准模块 (FRM) 和三明治融合模块,以实现有效的多尺度特征融合.
- 通过RFAConv模块和LSDECD检测头对模型进行了优化.
主要成果:
- 在VisDrone,TinyPerson和HIT-UAV数据集上,CF-YOLO在50% IoU (mAP50) 的平均平均精度上取得了显著的改进.
- 与基线模型和其他最先进的方法相比,表现出卓越的性能.
- 具体改进:在VisDrone上为12.7%,在TinyPerson上为10.1%,在HIT-UAV上为3.5%.
结论:
- 拟议的CF-YOLO算法有效地提高了无人机图像中的小目标检测.
- 新的CS-FPN,FRM和三明治融合模块有助于改进特征表示和融合.
- CF-YOLO为挑战远程传感小型目标检测任务提供了强大的解决方案.
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