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在光学遥感中提高检测性能图像对象检测:采用空间自适应角度感知网络和边缘感知斜边界框损失功能的双重策略

Zexin Yan¹, Jie Fan¹, Zhongbo Li¹

¹Institute of System Engineering, Academy of Military Sciences, Beijing 100141, China.

Sensors (Basel, Switzerland)

|August 29, 2024

概括

空间自适应角度感知 (SA3) 网络通过改进旋转边界框角度来改善光学遥感对象检测. 这种方法提高了准确性,特别是在高交叉点超过联盟 (IoU) 值时,使用一种新的边缘感知斜边界框损失 (EAS损失).

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:

在光学遥感图像中对象检测面临的挑战是不连续的边界,限制了在高交叉处的准确性.
现有的方法难以精确优化旋转边界框的角度参数,阻碍了复杂场景中的性能.

研究的目的:

引入一个新型网络,即空间自适应角度感知 (SA3) 网络,旨在提高光学遥感中的物体检测精度.
解决不连续边界的局限性,并提高性能,特别是在高IOU值时.
为旋转边界框开发一个有效的角度回归损失函数.

主要方法:

提出了空间自适应角度意识 (SA3) 网络,采用分层精细化方法 (粗放回归,精细回归,精确调整) 进行旋转边界框角度优化.
引入了基于高斯变换的IoU因子,并开发了边缘意识的斜边界框损失 (EAS损失) 来增强角度回归.
实施了阶级意识和阶级无意识的策略,以适应特定任务.

主要成果:

SA3网络显著提高了检测准确度,特别是在高IOU值时.
在角度回归的最后阶段,EAS损失增强了损失梯度,提高了训练效率和度量调整.
综合SA3网络和EAS损失将ReBiDet模型在DOTA-v1.5上的平均平均精度 (mAP) 提高到78.85%,在高IOU条件下获得了实质性的收益.

结论:

通过改进角度参数,SA3网络有效地解决了旋转物体检测方面的挑战.
EAS Loss在训练效率和角度回归的准确性方面提供了显著的改进.
提出的方法大大提高了现有的物体检测模型的性能,如ReDet和ReBiDet,特别是在要求高IOU的场景中.

关键词:

不连续的边界线是不连续的对象检测检测对象检测对象检测光学遥感是一种远程传感技术.旋转等价变量是旋转等价变量.

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