CRDet: An Object-Context-Aware Detection Network for Oriented Object in Aerial Images

With the increasing of aerial remote sensing imagery data, object detection in aerial images has become a specific and active topic in remote sensing and computer vision areas. Although great progress has been made, there still exists challenges for object detection of small size, arbitrary orientations, and dense distribution. To address these problems, we propose a novel detector named CRDet (object-Context-aware Rotated object Detector) in this paper. Our CRDet is mainly consists of two modules, RRGM (the Rotated Region of Interests Generation module) and OCIEM (Object Context Information Extraction Module). Specifically, the RRGM based on affine transformation is devised to improve the detection effect of objects with dense distribution and arbitrary orientations. The OCIEM is designed to improve the detection effect for small objects. The network with the two modules is designed with analysis. The proposed CRDet is tested on the challenging benchmark datasets and compared with several state-of-the-art methods. The analyzing and experimental results show that our proposed CRDet achieve superior performances, which clearly demonstrate its effectiveness.