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In remote sensing image (RSI) object detection, the oriented bounding box (OBB) can accurately locate objects with arbitrary orientation and obtain orientation information. The detection based on OBB is still a challenging task. In RSI, the distribution of objects is extremely uneven, which causes aggregation to occur. Some researchers believe that the characteristic of dense distribution is a reason for the difficulty of object detection. However, there are no in-depth experimental studies on this. This paper proposes an OBB-based dense object determination method, which determines the dense objects in datasets by two conditions consisting of interclass distance, intraclass distance, minimum distance between objects, and minimum edge length of objects. The experimental results of dense and non-dense object detection concludes that the characteristics of dense distribution in RSI do not easily cause the objects to be more difficult to detect. To make full use of the object features, we propose a second-stage detection head named RoIF-Net, in which we extract region of interest (RoI) from the input image and fuse it with the RoI extracted from feature maps to add detail features, and construct a feature induction module based on self-attention mechanism to achieve position regression and category classification. This structure can be used in any two-stage network to enhance detection capabilities. Using our method on three credible and challenging datasets, DOTA, DIOR-R, and UCAS-AOD, we obtained 81.80%, 68.49%, and 90.25% mAP, respectively, reaching SOTA based on OBB detection, proving the effectiveness and advancement of our method.

Recently, object detection in remote sensing images (RSIs) have received extensive attention and made significant progress. Nonetheless, the arbitrary orientations of objects in RSIs make their detection a challenging task. Most of the existing detection methods are difficult to extract the orientation features of objects due to the lack of directionality of conventional convolutions. In addition, the boundary discontinuity in angle regression affects the detection of object orientations. In response to these problems, this article proposes an orientation-first refinement detector (OFRDet), which is based on a strategy that enables the detector to detect the angle of an object ahead of others and presets oriented anchors. In OFRDet, we propose an angle encoding regression module (AERM) and an angle channel attention module (ACAM). AERM transforms angle detection into multiparameter regression, which eliminates boundary discontinuities. ACAM uses convolution kernels with different angles to extract directional features purposefully according to the preset oriented anchors. After these two modules, more accurate bounding boxes are generated and sent to the refined stage to obtain the final detection results. We evaluate our method and demonstrate the effectiveness of it by conducting experiments on two challenging and credible datasets, DOTA, HRSC2016. OFRDet achieves competitive results 79.56%, 96.29% mAP on the two datasets, respectively.