Rethinking Label Assignment and Sampling Strategies for Two-Stage Oriented Object Detection

Jinjin Qian, Chengxu Liu, Yubin Ai, Guoshuai Zhao, Xueming Qian

Abstract: Oriented object detection seeks to determine both the position and orientation of objects, yet angle periodicity often limits performance. To solve this issue, we rethink label assignment and sampling strategies and propose a pair of orientation-aware assigner and sampler (OAS) for a two-stage detector. The orientation-aware assigner (OA) incorporates angle and location to improve positive and negative sample assignment, while the orientation-aware sampler (OS) ranks positive samples by their angular difference from ground truth, adjusting learning weights by soft sampling. Such a design significantly mitigates the angle periodicity problem and enables detector focusing on high-quality samples with a more consistent orientation for training. Experimental results on two challenging oriented object detection benchmarks demonstrate that OAS can consistently boost the detection accuracy based on many existing two-stage detectors (e.g., Oriented R-CNN and RPGAOD) without additional cost. Both code and pretrained models are available at https://github.com/skyandkibo/OAS.

External IDs:dblp:journals/lgrs/QianLAZQ25