ClearSight-RS:一个基于YOLOv5的网络,具有动态增强功能,用于远程传感小目标检测.
Jie Yuan1,2, Shuyi Feng1,2, Hao Han1
1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210024, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
改进的YOLOv5网络ClearSight-RS通过集成新型模块来提高远程传感图像中的小目标检测,以获得更清晰的特征感知和准确的定位. 它在基准数据集上显著优于现有的方法.
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:
- 由于复杂的背景,薄弱的特征和尺度变化,遥感中检测小目标具有挑战性.
- 现有的方法难以准确地识别和定位杂乱中的小物体.
研究的目的:
- 开发一个改进的YOLOv5网络,ClearSight-RS,用于远程传感中增强小目标检测.
- 为了改善小物体的特征提取,目标聚焦和背景抑制.
主要方法:
- 在骨干中集成了改进的动态蛇卷积 (DSConv) 模块,用于边界和纹理特征提取.
- 在Neck中嵌入双层路由注意 (BRA) 模块,以更好地聚焦目标和压制背景.
- 通过使用浅,高分辨率的特征层来优化检测头.
主要成果:
- 在VEDAI数据集中,ClearSight-RS在所有8个车型类别中实现了最高的mAP.
- 在NWPU VHR-10数据集上实现了93.8%的整体mAP,超过了Faster RCNN和YOLOv5l.
- 证明了BRA模块在抑制背景干扰和在DOTA数据集上捕获小目标特征方面的有效性.
结论:
- 在复杂的遥感背景下,ClearSight-RS有效地平衡了在复杂的遥感背景下检测小目标的准确性和效率.
- 拟议的网络在检测车辆和多类小目标方面表现突出.
- ClearSight-RS网络验证了其在挑战远程传感图像分析任务方面的有效性.
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