  Objects in aerial images are typically embedded in complex backgrounds and
exhibit arbitrary orientations. When employing oriented bounding boxes (OBB) to
represent arbitrary oriented objects, the periodicity of angles could lead to
discontinuities in label regression values at the boundaries, inducing abrupt
fluctuations in the loss function. To address this problem, an OBB
representation based on the complex plane is introduced in the oriented
detection framework, and a trigonometric loss function is proposed. Moreover,
leveraging prior knowledge of complex background environments and significant
differences in large objects in aerial images, a conformer RPN head is
constructed to predict angle information. The proposed loss function and
conformer RPN head jointly generate high-quality oriented proposals. A
category-aware dynamic label assignment based on predicted category feedback is
proposed to address the limitations of solely relying on IoU for proposal label
assignment. This method makes negative sample selection more representative,
ensuring consistency between classification and regression features.
Experiments were conducted on four realistic oriented detection datasets, and
the results demonstrate superior performance in oriented object detection with
minimal parameter tuning and time costs. Specifically, mean average precision
(mAP) scores of 82.02%, 71.99%, 69.87%, and 98.77% were achieved on the
DOTA-v1.0, DOTA-v1.5, DIOR-R, and HRSC2016 datasets, respectively.
