改进的YOLOv11n用于无人机图像中的小物体检测:使用更少的参数更高的准确性
1Shanghai DianJi University, Shanghai, 201306, China.
Scientific reports
|January 18, 2026
概括
本研究引入了一种增强的YOLOv11模型,用于在无人机图像中改进对象检测,显著提高小型对象的精度并减少计算负载. 新型号在多个数据集中实现了卓越的性能,在具有挑战性的空中条件下证明了其有效性.
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 (UAV) 图像中对象检测受到高空捕获挑战的阻碍.
- 这些包括密集的目标,高比例的小物体和有限的机载处理能力,导致准确性差,错误/错误检测增加.
研究的目的:
- 提出一个改进的YOLOv11模型,解决无人机图像中对象检测的局限性.
- 提高检测准确度,特别是对于小型和密集分布的物体,同时优化计算效率.
主要方法:
- 在骨干中设计了一个多级边缘特征自适应选择 (MSEAF) 模块,以处理小物体和弱边缘信息.
- 用ScalCat和Scal3DC模块重建了部,并整合了P2小型物体检测头,以更好地利用高分辨率的信息.
- 开发了一个共享的,重参数化的轻量级检测头 (SRepD),以减少计算冗余并改进功能融合.
主要成果:
- 在VisDrone2019上,该模型比YOLOv11n提高了mAP50的4.6%,精度提高了4.6%,参数减少了8.5%.
- 在TinyPerson上,mAP50和Precision分别增加了5.5%和5.6%,参数减少了7.7%.
- 在mAP50 (3.8%的增益) 和精度 (3.2%的增益) 上表现优于YOLOv11s,仅使用其参数的25%,并且在HazyDet数据集上表现优越.
结论:
- 建议改进的YOLOv11模型有效地解决了无人机物体检测方面的关键挑战.
- 整合MSEAF,ScalCat,Scal3DC,P2头和SRepD模块,可以显著提高准确性和效率.
- 该模型在各种数据集中表现出卓越的性能,包括那些具有小物体和模糊条件的数据集,突出其实际适用性.
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