一个对象检测模型AAPW-YOLO用于无人机远程传感图像,基于自适应卷积和重建的功能融合模型
Yiming Wu1, Xiaofang Mu2, Hong Shi1
1School of Computer Science and Technology, Taiyuan Normal University, Taiyuan, 030000, China.
Scientific reports
|May 9, 2025
概括
本研究介绍了AAPW-YOLO,这是一种新的小型物体检测模型,可以增强无人机空中图像和遥感的特征提取. 该模型以更少的参数实现更高的准确性,提高了对具有挑战性的数据集的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 在空中图像中检测小物体面临诸如尺度变化和背景干扰等挑战.
- 现有的模型在微小物体的高效特征提取方面扎.
研究的目的:
- 开发一个改进的小型物体检测模型 (AAPW-YOLO) 用于航空图像.
- 增强特征提取和融合,以提高准确性和效率.
主要方法:
- 在YOLOv8骨干中实现可变内核卷积 (AKConv),用于多尺度的特征捕获.
- 引入了注意力尺度序列融合P2 (ASFP2) 以优化部的特征融合.
- 使用智能交叉与联盟 (Wise-IoU) 损失,以提高回归精度.
主要成果:
- 在VisDrone2019和DOTA v1.0数据集上,模型参数数量减少了30%.
- 在VisDrone2019上提高了3.6%的平均精度 (mAP@0.5),在DOTAv1.0.0上提高了2.5%,在VisDrone2019上提高了3.6%.
- 证明了增强的识别准确性,稳定性和概括能力.
结论:
- 在航空图像中,AAPW-YOLO有效地解决了小物体检测方面的挑战.
- 与现有方法相比,该模型在准确性和效率之间提供了更好的平衡.
- 提出的技术有助于计算机视觉在遥感应用中的进步.
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