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在遥感图像中检测微小物体的学习几何Jensen-Shannon分歧
Shuyan Ni1, Cunbao Lin1, Haining Wang2,3
1Department of Electronic and Optical Engineering, Space Engineering University, Beijing, China.
Frontiers in neurorobotics
|November 29, 2023
概括
在远程传感图像中检测微小物体是具有挑战性的. 一个新的网络JSDNet使用几何Jensen-Shannon (JS) 分歧来通过将对象建模为高斯分布来改进微小对象检测.
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:
- 遥感图像中的微小物体由于其有限的像素表示而存在独特的检测挑战.
- 标准的物体探测器在特征提取方面遇到了困难,并且对小物体的交叉-超过-联盟 (IoU) 值敏感.
研究的目的:
- 开发一个专门的探测器,JSDNet,以克服一般物体探测器在微小物体检测方面的局限性.
- 为了提高遥感数据中微小物体检测的准确性和稳定性.
主要方法:
- 将Swin变压器集成为增强微小物体特征提取的骨干.
- 模拟框和地面真相作为2D高斯分布,用于微小对象的统计表示.
- 介绍了JSDM模块,利用几何JS分歧进行强大的箱回归,减轻IOU的敏感性.
主要成果:
- 在检测微小物体方面,JSDNet表现出卓越的性能.
- 拟议的方法在基准数据集 (AI-TOD和DOTA) 上表现优于最先进的一般物体探测器.
结论:
- 通过采用一种基于统计分布的新方法,JSDNet有效地解决了微小物体检测的挑战.
- 该网络的设计,包括Swin变压器和JS分歧,在远程传感图像分析中取得了重大进展,用于小型物体识别.
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