HSF-YOLO:一个多尺度和梯度感知网络,用于远程传感图像中的小物体检测
Fujun Wang1,2, Xing Wang3
1School of Geomatics, Liaoning Technical University, Fuxin 123000, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究介绍了HSF-YOLO,这是一个用于远程传感图像 (RSI) 中小物体检测的增强框架. 通过完善特征编码,注意力机制和本地化,HSF-YOLO显著提高了准确性,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 在遥感图像 (RSI) 中,小物体检测 (SOD) 面临规模变化,遮蔽和复杂背景等挑战.
- 现有的方法往往会导致小物体的高错误和错误检测率.
研究的目的:
- 提出一个新的检测框架,HSF-YOLO,用于在RSI中增强小物体检测.
- 为了改善功能编码,注意力交互和YOLOv8骨干中的定位精度.
主要方法:
- 引入了混合门增强卷积 (HAEC),以捕获多尺度的语义和局部信息.
- 实现了一个空间交互混动注意模块 (C2f_SIS),以增强功能交互和噪音抑制.
- 开发了焦梯度精炼损失 (FGR-Loss),以提高回归精度和训练稳定性.
主要成果:
- 在三个公共数据集 (VisDrone2019,DIOR,NWPU VHR-10) 上,HSF-YOLO显示出与基线YOLOv8相比的显著改善.
- 在VisDrone2019数据集上实现了mAP@0.5 (高达5.7%) 和mAP@0.5:0.95 (高达4.0%) 的显著增长.
- 在不同RSI数据集中展示了一致的绩效改进,证实了有效性.
结论:
- 在复杂的RSI场景中,HSF-YOLO为小型物体检测提供了统一有效的解决方案.
- 该框架在检测准确性和计算效率之间提供了有利的平衡.
- 拟议的模块有助于克服RSI当前SOD技术的局限性.
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