RMK RetinaNet：旋转多核RetinaNet用于遥感影像中鲁棒的旋转物体检测
RMK RetinaNet: Rotated Multi-Kernel RetinaNet for Robust Oriented Object Detection in Remote Sensing Imagery
AI总结：
RMK RetinaNet通过多尺度核块、多方向上下文锚点注意机制和欧拉角编码模块，提升遥感影像中旋转物体检测的鲁棒性和性能。
摘要：
在遥感影像中进行旋转物体检测受到三个主要瓶颈的限制：非适应性感受野利用、不足的长距离多尺度特征融合以及角度回归的不连续性。为了解决这些问题，我们提出了旋转多核RetinaNet（RMK RetinaNet）。首先，我们设计了多尺度核（MSK）块以加强适应性多尺度特征提取。其次，我们将多方向上下文锚点注意机制（MDCAA）融入特征金字塔中，以增强跨尺度和方向的上下文建模。第三，我们引入了自上而下的路径以保留常被降采样过程降质的细粒度空间细节。最后，我们开发了欧拉角编码模块（EAEM）以实现连续且稳定的角回归。在DOTA-v1.0、HRSC2016和UCAS-AOD上的大量实验表明，RMK RetinaNet在性能上与最先进的旋转物体检测器相当，同时在多尺度和多方向场景中提高了鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
