Learning Boundary Continuity-Aware Gaussian Encoder for Oriented Object Detection
针对旋转目标检测中边界不连续问题，提出边界连续性感知高斯编码器（BCGE），直接预测高斯分布并学习旋转框为二维矩阵，可替换现有检测器的编码模块，在五个数据集上验证了有效性。
Oriented object detection has been crucial for rotation-sensitive tasks and has garnered significant attention. Most existing methods generate angles as detector output vectors, but this strategy can abnormally magnify visually similar differences between two boxes in certain circumstances, termed boundary discontinuity issue. To overcome this limitation, we propose a boundary continuity-aware Gaussian encoder (BCGE). Specifically, BCGE directly predicts target Gaussian distributions for proposals and learns an oriented bounding box as an integrated 2-D matrix, effectively addressing boundary discontinuity issues. We also propose a transformation from Gaussian representation back to boxes and extend this transformation theory to the complex domain to adapt to the learning characteristics of neural networks. Furthermore, BCGE serves as a versatile plug-and-play architectural encoder, directly replacing the standard coding process in various oriented detectors with adaptability. Experimental results on five popular datasets, i.e., DOTA, UCAS-AOD, HRSC2016, SSDD, and HRSID, consistently show the effectiveness of our approach.