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在无人机空中视图中基于轻量级OSD-YOLOv10的车辆检测
Yang Zhang1,2, Xiaobing Chen3,4, Su Sun1,2
1Huaiyin Institute of Technology, College of Computer and Software Engineering, Huaian, 223003, China.
Scientific reports
|July 11, 2025
概括
通过改进小目标特征提取和减少模型大小,OSD-YOLOv10提高了无人机空中图像中的车辆检测. 这种轻量级算法为实时应用提供了卓越的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的无人机成像车辆检测模型面临的挑战是性能低,目标特征提取小,参数大.
- 有效和准确的物体检测对于使用无人机 (UAV) 的自主系统和监视至关重要.
研究的目的:
- 开发一个基于YOLOv10的优化算法 (OSD-YOLOv10),用于在无人机空中图像中增强车辆检测.
- 解决小型目标检测的局限性,并减少在资源有限的无人机硬件上部署的计算复杂性.
主要方法:
- 通过在线卷积再参数化引入了OCRConv模块和轻量级的SPCC结构,取代了C2f模块.
- 集成了一个双层前混合注意模块和一个双层小型目标检测层,结合了浅层特征.
- 实现了DySample动态上采样模块,以改善子网络中的特征融合.
主要成果:
- OSD-YOLOv10实现了参数减少40.7%,浮点运算减少3.6%.
- 在VisDrone-DET2019和UAVDT数据集上,准确度 (+1.3%) 和平均精度 (+1.6%) 的改善.
- 在检测准确性和计算复杂性方面表现优于其他YOLO系列和轻量级模型.
结论:
- 基于无人机的车辆检测中,OSD-YOLOv10提供了高精度和低资源消耗之间的最佳平衡.
- 拟议的算法非常适合在无人机的机载硬件上部署,可实现高效的实时目标检测.
- 这些进步有助于利用空中图像制造更有能力,更高效的自主系统.
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