一个弱监督的面向对象探测器:基于知识的dropblock和统一的回归网络
Lijuan Duan1, Zichen Zhang2, Zhaoying Liu3
1College of Computer Science, Beijing University of Technology, Beijing, 100124, China; Chongqing Research Institute, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Trusted Computing, Beijing University of Technology, Beijing, 100124, China.
概括
这项研究介绍了KDUNet,这是一款用于遥感图像的新型弱监控物体探测器. 通过强调整个物体和统一旋转和水平边界框,KDUNet提高了定位准确性,优于完全监督的方法.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 远程传感图像 (RSI) 中的对象检测通常使用面向边界框 (RBoxes),比水平框 (HBoxes) 更加劳动密集.
- 目前存在的HBoxes检测器的监控很弱,通常专注于对象的区分部分,从而降低了定位准确性.
- 弱监控方法中的空间转换会在RBoxes和HBoxes之间产生模两可,阻碍近距离物体的检测.
研究的目的:
- 提出一种新的弱监督物体探测器,KDUNet,它可以学习高质量的特征,并解决RBoxes和HBoxes之间的差异.
- 通过强调整个对象而不是仅仅是区分部分来提高对象定位的准确性.
- 为RBoxes和HBoxes开发统一的回归方法,以减轻检测模两可.
主要方法:
- KDUNet利用远距离的背景信息和多种道输入来掩盖有歧视性的对象部分,从而促进对整个对象的关注.
- 引入了一种新的界限框距离测量方法,通过一个围绕的矩形和转换角度统一RBoxes和HBoxes,用于高斯距离评估.
- 网络被训练来学习高质量的特征信息,并弥补不同界限框类型之间固有的模糊性.
主要成果:
- KDUNet展示了学习高质量的特征信息的能力,并有效减少对象检测中的模两可.
- 在DIOR数据集上,KDUNet的平均平均精度 (mAP) 为57.8%,超过了6个完全监督的网络.
- 在HRSC数据集中,KDUNet的平均平均精度 (mAP) 为90.1%,超过了6个完全监督的网络.
结论:
- 在远程传感图像的弱监督物体检测方面,KDUNet提供了显著的进步.
- 提出的方法有效地解决了现有方法的局限性,从而提高了准确性和稳定性.
- KDUNet的性能验证了其在远程传感图像分析中的实际应用潜力.
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