SEB-YOLO:一个改进的YOLOv5模型用于远程传感小目标检测
Yan Hui1,2, Shijie You1,2, Xiuhua Hu1,2
1School of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究介绍了SEB-YOLO,这是一个改进的YOLOv5算法,用于远程传感目标检测. 它提高了小物体和类似物体的性能,在复杂的场景中实现了更高的精度.
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:
- 遥感目标检测面临着小物体和类似目标的挑战,导致性能差.
- 现有的算法在复杂的遥感图像中与语义信息提取和特征丢失作斗争.
研究的目的:
- 提出一个改进的YOLOv5算法 (SEB-YOLO),用于增强远程传感图像目标检测.
- 解决在遥感数据中检测小物体和区分类似目标方面的局限性.
主要方法:
- 使用空间到深度 (SPD) 和卷积 (Conv) 层 (SPD-Conv) 重建了骨干网络,以保持全球特征.
- 在脊柱的最后一个聚合层中整合了注意力机制,以改进目标识别和定位.
- 利用双向特征金字塔网络 (Bi-FPN) 采用双线互插来增强特征融合和跨尺度连接.
- 引入了脱头,以改善模型的融合,并解决分类回归任务冲突.
主要成果:
- 在NWPU VHR-10数据集上,SEB-YOLO实现了93.5%的平均平均精度 (mAP).
- 在RSOD数据集上,SEB-YOLO实现了93.9%的mAP.
- 在各自的数据集上,与原来的YOLOv5l算法相比,显著改善了4.0%和5.3%.
结论:
- 拟议的SEB-YOLO算法显著提高了复杂的遥感图像中的目标检测性能.
- 集成SPD-Conv,注意力机制,Bi-FPN和脱头,提高了模型检测小物体和类似物体的能力.
- SEB-YOLO为远程传感图像分析和目标识别提供了更强大的解决方案.
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