Category-Aware Dynamic Label Assignment With High-Quality Proposals for Oriented Object Detection

Oriented objects in images are typically embedded in complex backgrounds and exhibit arbitrary orientations. When using oriented bounding boxes (OBBs) to represent these objects, the periodicity of the angles and associated variations in side lengths lead to discontinuities in the angle loss. This paper fundamentally addresses this problem by proposing a trigonometric loss function in the complex plane. Moreover, a conformer RPN head is designed with convolution and multi-head self-attention, which can dynamically capture angular and classification 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 oriented proposal label assignment. This method makes negative sample selection more representative, ensuring consistency between classification and regression features. Experiments were conducted on five 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%, 46.45%, and 98.77% were achieved on the DOTA-v1.0, DOTA-v1.5, DIOR-R, STAR, and HRSC2016 datasets, respectively.