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Learning Boundary Continuity-Aware Gaussian Encoder for Oriented Object Detection

delete2025-07-01
delete0
PRE
AI
H
Hongmin Liu
赵
赵成义 (Chengyi Zhao)
樊
樊彬 (Bin Fan)
Z
Ziyi Liu
胡雨凡 封面图
胡雨凡 (Yufan Hu)
DOI：10.1109/TCYB.2025.3562555delete
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摘要

摘要

En 中文
目标方向检测对于旋转敏感任务至关重要，并已引起广泛关注。大多数现有方法将角度作为检测器输出向量生成，但这种策略在某些情况下会异常放大两个框之间的视觉相似差异，即边界不连续性问题。为克服这一局限性，我们提出了边界连续性感知的高斯编码器（BCGE）。具体来说，BCGE直接预测候选框的目标高斯分布，并将有向边界框学习为集成的2-D矩阵，有效解决了边界不连续性问题。我们还提出了从高斯表示转换回框的方法，并将此转换理论扩展到复数域，以适应神经网络的学习特性。此外，BCGE作为一个通用的即插即用架构编码器，能够直接替换各种有向检测器中的标准编码过程，具有适应性。在五个流行数据集（即DOTA、UCAS-AOD、HRSC2016、SSDD和HRSID）上的实验结果一致表明了我们方法的有效性。
Keyword:
Boundary discontinuity issue
Gaussian distribution
oriented object detection

期刊

IEEE Transactions on Cybernetics 封面图
IEEE Transactions on Cybernetics
IF:
10.5
论文数:
1.1W
被引数:
5.0W

机构

U
university of science and technology beijing
学者数:
1.3W
论文数: 4.5K
被引数: 2
引用论文

引用论文

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