# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 1 Introduction
In conclusion, the existing methods for rotating convolutions and bounding box regression have not adequately tackled the problem of aligning the feature and sample levels of detection networks with the angular spatial traits of targets. More precisely, at the feature level, it is difficult for the angles of rotating convolution kernels to align with the angles of targets to accommodate their angular variations. As depicted in Figure 1a, approaches that pre-set fixed rotation angles have difficulty in achieving a balance between computational cost and the number of pre-set angles. Having an excessive number of pre-set angles escalates the computational burden, while having too few can result in inaccurate extraction of directional features. In contrast, our proposed method adopts a data-driven strategy to dynamically acquire the angles, generating a coarse representation of the target's direction within the image. This angle would guide the rotation of the convolution kernels to precisely capture the target's directional features. At the sample level, anchor boxes must be aligned with the orientation of the target and avoid problems arising from inconsistent directionality in angle regression during training, the cyclical nature of angle changes (from 360 degrees back to 0 degrees), and the substantial variations when regressing high-aspect-ratio (slender) anchor boxes from their pre-set positions to the target positions, which can give rise to drastic changes in the loss function and cause unstable training. Figure 1b highlights the challenges of current methods that regress anchor boxes using angles. When angle regression is employed for anchor boxes, two scenarios may occur: First, a minor clockwise rotation of the red box could align it with the target box (green box), as indicated by the rotation of the yellow box. Second, a significant counterclockwise rotation of the red box could align it with the target box (green box), as shown by the rotation of the blue box. Encountering the second scenario can lead to a problem where the large rotation angle causes drastic changes in the loss function. Our proposed method, however, abandons the use of angles for the direction regression of anchor boxes, choosing instead to directly use two offset values to regress the center points of each side of the original anchor box. This approach avoids the difficulties in regression due to inconsistent rotation directions and the challenges related to rotating high-aspect-ratio anchor boxes. Through the analysis, it is clear that no matter how we design methods for rotating convolutions and anchor box regression, we cannot avoid the challenge of aligning feature and sample information with the spatial orientation of targets in RS detection tasks. Compared to existing rotating detection networks, it is particularly important to develop a dynamic, target-angle-sensitive feature extractor and an anchor box regress  
Figure 1: Our motivation for this work originates from two crucial aspects: (**a**) In the detection of rotating targets, instances of targets with similar visual expressions are placed in arbitrary directions (for example, ships). Therefore, it is reasonable to learn the direction of the target in a data-driven manner and rotate the convolutional kernels accordingly. (**b**) Existing methods are troubled by the inconsistencies in regression direction and significant changes in features extracted from targets when rotating high-aspect-ratio anchor boxes. Thus, approximating the rotation direction of the anchor box by regressing towards the boundary center region is a solution to the problem of large changes in target features when rotating anchor boxes with high aspect ratios. sensitive to angles. To address this, we introduce the Dynamic Direction Learning R-CNN (DDL R-CNN), which is composed of two main parts: the dynamic direction learning (DDL) module and the Boundary Center Region Offset Proposal Network (BC-ROPN).