Learning Boundary Continuity-Aware Gaussian Encoder for Oriented Object Detection

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.