A Double-Head Global Reasoning Network for Object Detection of Remote Sensing Images

Object detection of remote sensing images is a fundamental and important task for a wide range of applications, such as agriculture, military, and geological exploration. However, it is still a challenging task due to complex background, heavy occlusion, and object ambiguities. Complex remote sensing scenes contain rich visual and spatial relationships between proposals, while most existing methods ignore these relationships. In this article, we introduce a novel double-head global reasoning network (DGRN), which endows an object detection model with the ability to adapt global reasoning by propagating visual and spatial embeddings between positive proposals (foreground) and negative proposals (background). Instead of propagating the features of positive proposals, we evolve high-level embeddings globally to explore potential information in all proposals. Specifically, our method first classifies and locates positive proposals and negative proposals simultaneously, and then adaptively learns two sparse region-to-region undirected graphs for classification and location, respectively. Finally, the graph reasoning module (GRM) conducts the propagation of proposals' embedding to improve the performance of object classification and localization. Without any prior knowledge, our DGRN method explores reasoning graphs between proposals for object classification and location, respectively, and then uses graph convolutional neural network (GCN) to propagate information and achieve relational reasoning. Our method is light-weighted and flexible enough to enhance many object detection models, making object detection models the capability of relational reasoning. The experimental results on DOTA and DIOR showed that the proposed method had better detection performance than object detection networks without considering relationships.