提升遥感图像中旋转目标检测的鲁棒性，解决尺度与角度不连续问题。
RMK RetinaNet: Rotated Multi-Kernel RetinaNet for Robust Oriented Object Detection in Remote Sensing Imagery
- 设计多尺度核块自适应提取多尺度特征
- 在多方向注意力机制下实现跨尺度上下文建模
- 通过欧拉角编码实现角度回归连续稳定
遥感图像中的旋转目标检测面临三大瓶颈：感受野利用不自适应、长距离多尺度特征融合不足、角度回归存在不连续性。为此，我们提出旋转多核RetinaNet（RMK RetinaNet）。首先，设计多尺度核（MSK）模块强化自适应多尺度特征提取；其次，将多方向上下文锚点注意力（MDCAA）引入特征金字塔，增强跨尺度与多方向的上下文建模能力；第三，引入自底向上的路径以保留下采样过程中易丢失的细粒度空间细节；最后，提出欧拉角编码模块（EAEM），实现连续且稳定的角度回归。在DOTA-v1.0、HRSC2016和UCAS-AOD数据集上的大量实验表明，RMK RetinaNet性能达到当前先进水平，同时在多尺度与多方向场景下显著提升鲁棒性。
原文摘要 · Abstract (English)
Rotated object detection in remote sensing imagery is hindered by three major bottlenecks: non-adaptive receptive field utilization, inadequate long-range multi-scale feature fusion, and discontinuities in angle regression. To address these issues, we propose Rotated Multi-Kernel RetinaNet (RMK RetinaNet). First, we design a Multi-Scale Kernel (MSK) Block to strengthen adaptive multi-scale feature extraction. Second, we incorporate a Multi-Directional Contextual Anchor Attention (MDCAA) mechanism into the feature pyramid to enhance contextual modeling across scales and orientations. Third, we introduce a Bottom-up Path to preserve fine-grained spatial details that are often degraded during downsampling. Finally, we develop an Euler Angle Encoding Module (EAEM) to enable continuous and stable angle regression. Extensive experiments on DOTA-v1.0, HRSC2016, and UCAS-AOD show that RMK RetinaNet achieves performance comparable to state-of-the-art rotated object detectors while improving robustness in multi-scale and multi-orientation scenarios.
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