# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 1 Introduction  
RS is a technology that automatically collects information on the Earth's surface from a distance using sensors mounted on satellites, aircraft, or drones. It is widely used in environmental monitoring, urban planning, agricultural management, military reconnaissance, disaster response, and geological exploration. This technology offers several advantages, including a broad coverage area, strong real-time data acquisition capabilities, and high cost-effectiveness. In the research on RS image target detection, the detection methods for rotating targets are critical [1, 2, 3]. Traditional detection methods often rely on horizontal anchor boxes, such as the SSD [4], YOLO [5, 6, 7], and Faster-RCNN [8] detection networks. This can cause misalignment between the target and the proposal region when dealing with rotatingtargets, leading to a decrease in detection accuracy. To address this issue, researchers have proposed various methods involving rotating anchor boxes to improve the performance of RS detection networks. One approach is to use rotating anchor points instead of traditional horizontal anchor points to match the orientation of the target. For example, Rotated RPN [9] improves detection accuracy by pre-setting anchor points at different angles to cover the various directions in which targets may appear. Furthermore, certain techniques mitigate the impact of horizontal proposal regions by directly regressing the coordinates of the detection box's four vertices. An example of this is the Gliding Vertex [10] method, which can accommodate targets of arbitrary orientations. Although methods involving rotating anchor points and vertex regression have improved the detection performance of rotating targets, they still face issues with computational complexity. To enhance detection efficiency, several studies are investigating the reduction of computational complexity without compromising or even improving the accuracy of detection. This includes developing new loss functions to train better networks, as well as designing more efficient network structures to handle rotating targets. In the realm of RS detection, the focus of many researchers is on enhancing the network's ability to capture the directional characteristics of targets during the feature extraction phase. To transcend the constraints inherent in conventional convolutional neural networks, which struggle to recognize orientation features when dealing with rotating targets, approaches involving rotating convolution kernels and rotating anchor boxes have been introduced. These techniques adjust the orientation or shape of the convolution kernels to match the arbitrary rotation angles of targets to enhance detection accuracy. For instance, the R2 CNN [11] method identifies rotating targets by detecting the first two corners in a clockwise sequence along with the rectangle's height. CenterNet [12], on the other hand, locates the center of the target through keypoint detection and then expands outward from this center to determine the target's bounding box. These innovative approaches not only refine the convolution operation and the regression of anchor boxes for more comprehensive target recognition but also drive the evolution of network architectures. This includes the adoption of direction-sensitive convolution kernels and rotating pooling layers, which significantly boost the model's adaptability to rotational changes and its recognition capabilities. However, current technologies still grapple with challenges in the design of feature extractors. In particular, there is a need to strike an appropriate balance between the number of rotation angles and the resulting increase in computational costs. Additionally, there are issues with the fluctuations in the loss function when rotating anchor boxes with very high aspect ratios.