Dual-Stream Global Relationship Learning for Oriented Object Detection in Remote Sensing Images

Oriented object detection has attained remarkable progress in addressing the challenges associated with rotating invariant feature extraction. However, most existing object detection most existing encounter serious performance degradation when processing objects with tiny size, blurring, and occlusion. One of the reasons is that existing methods mainly focus on local features, while overlooking the correlation among objects and common sense knowledge, which is inconsistent with the human visual system. To address this issue, we propose a dual-stream global relationship learning method consisting of two modules: a dynamic correlation learning (DCL) method and a global knowledge mapping (GKM) module. The DCL method can construct a region-to-region dynamic relationship graph based on feature correlations and implicitly guides the detection network to learn more powerful class representation by updating nodes of the graph. The GKM module generates a class-to-class global semantic relationship graph via external knowledge and achieve more stable representation learning by dynamically global relational mapping. Extensive experiments are performed on oriented object detection datasets DOTA, HRSC2016, DIOR-R as well as horizontal object detection datasets NWPU VHR-10, RSOD. The results demonstrate that the proposed method achieves the state-of-the-art detection accuracy.