旋转不变与对称感知像素差网络用于遥感目标检测
Rotation Invariant and Symmetry Aware Pixel Difference Network for Remote Sensing Object Detection
RIS-PDC 将旋转群先验和对称约束编码进卷积算子，用像素差和极谐空间聚合替代传统卷积，可直接替换骨干网络中的标准卷积，提升对旋转和遮挡的鲁棒性，且不增加参数量。
原文依据 introduces a novel convolutional operator called Rotation Invariant and Symmetry Aware Pixel Difference Convolution (RIS-PDC)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期遥感目标检测的进展主要集中于有向边界框设计和弱小目标特征增强，而往往忽略了遥感图像的内在几何属性，如旋转不变性和结构对称性。许多航空目标以多种朝向出现并呈现明显的对称模式，若未显式建模，在几何变化或部分遮挡下会导致检测失败和定位不准确。为此，我们提出了旋转不变与对称感知像素差网络（RIS-PiDiNet），引入了一种新的卷积算子——旋转不变与对称感知像素差卷积（RIS-PDC）。该算子以数学上严谨的公式替代传统卷积，编码了旋转群先验和对称约束。RIS-PDC 在极谐空间中利用像素差和对称引导聚合，使网络能够推断部分可见结构并推导被遮挡的对称部分。除了提升检测精度，RIS-PDC 通过将几何原理嵌入网络设计增强了模型可解释性。特征可视化展示了旋转一致的激活和对称完整的响应，揭示了网络即使在部分可见或朝向变化下如何捕捉底层目标结构。这产生了几何可解释的检测决策。据我们所知，RIS-PiDiNet 是首个在统一架构中联合纳入旋转不变性和对称性建模的遥感目标检测框架。在标准基准上的大量评估验证了其有效性，在 DOTA-v1.0（78.53% mAP 单尺度，81.81% 多尺度）、HRSC2016（98.60% mAP）和 DIOR-R（67.28% mAP）上达到最优性能，且计算开销可接受，参数量未增加。
英文原文摘要 2011 字符
Recent advancements in remote sensing object detection have predominantly focused on oriented bounding box design and small object feature enhancement, while often overlooking the intrinsic geometric properties of remote sensing images, such as rotation invariance and structural symmetry. Many aerial objects appear in multiple orientations and exhibit clear symmetrical patterns, which, if not explicitly modeled, can lead to detection failures and inaccurate localization under geometric variation or partial occlusion. To address this, we propose the Rotation Invariant and Symmetry Aware Pixel Difference Network (RIS-PiDiNet), which introduces a novel convolutional operator called Rotation Invariant and Symmetry Aware Pixel Difference Convolution (RIS-PDC). This operator replaces traditional convolution with a mathematically grounded formulation that encodes rotation group priors and symmetrical constraints. RIS-PDC utilizes pixel differences and symmetry-guided aggregation in the polar harmonic space, enabling the network to infer partially visible structures and deduce occluded symmetrical parts. Besides improving detection accuracy, RIS-PDC enhances model interpretability by embedding geometric principles into the network design. Feature visualizations demonstrate rotation-consistent activations and symmetry-complete responses, revealing how the network captures underlying object structure even under partial visibility or orientation changes. This yields geometrically interpretable detection decisions. To our knowledge, RIS-PiDiNet is the first remote sensing object detection framework that jointly incorporates rotation invariance and symmetry modeling within a unified architecture. Extensive evaluations on standard benchmarks validate its effectiveness, achieving state-of-the-art performance on DOTA-v1.0 (78.53% mAP single-scale, 81.81% multi-scale), HRSC2016 (98.60% mAP), and DIOR-R (67.28% mAP), all with acceptable computational overhead and no increase in parameter count.