EMFE-YOLO:用于无人机的轻量级小物体检测模型
Chengjun Yang1, Yan Shen1, Lutao Wang1
1School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
一个新的轻量级模型EMFE-YOLO通过改进特征提取和减少参数来提高无人机的小型物体检测. 这使得空中图像的精确分析在资源有限的无人机上是可行的.
科学领域:
- 计算机视觉
- 人工智能
- 机器人技术
背景情况:
- 在无人驾驶飞行器 (UAV) 的空中图像中检测小物体存在重大挑战,包括低精度和复杂的背景.
- 在资源有限的无人机上部署大参数物体检测模型在计算上是不可行的.
研究的目的:
- 提出一个轻量级的小型物体检测模型EMFE-YOLO,旨在有效地部署在无人机上.
- 在复杂的空中背景中提高小物体的检测精度,同时最大限度地减少模型参数.
主要方法:
- 通过改进YOLOv8s架构开发EMFE-YOLO.
- 整合了对大规模特征的增强注意力 (EALF) 结构,以关注大规模特征并改善小物体检测.
- 包括高效的多尺度特征增强 (EMFE) 模块用于特征提取和背景干扰减轻.
- 在网络部使用DySample来优化特征上采样.
主要成果:
- 与YOLOv8s相比,EMFE-YOLO显示了VisDrone2019-val数据集的显著改善,mAP50增加了8. 5%,mAP50:95增加了6. 3%.
- 该模型实现了参数的显著降低,相对于YOLOv8s降低了73%.
- 在检测准确性和计算效率之间实现了有利的平衡.
结论:
- EMFE-YOLO提供了一个可行的解决方案,用于准确和高效地检测无人机的空中图像中的小物体.
- 拟议模型的轻量级性质使其适用于有限的计算资源的无人机.
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