# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 1 Introduction  
Aerial object detection is used to automatically identify objects on surfaces or in air, such as buildings, roads, vehicles, etc. Its main purpose is to analyze aerial image data and to use them for resource management, environmental monitoring, urban planning, military reconnaissance, disaster management, etc. With the development of deep learning and GPU performance, the application of convolutional neural networks in RS detection is becoming increasingly widespread. The object detection framework is usually divided into one-stage [1; 2; 3] and two-stage [4; 5; 6] detectors. A one-stage detector utilizes a horizontal anchor box to generate candidate regions and then performs regression and classification operations to pinpoint the target accurately. SPP-Net [7] uses a spatial pyramid pooling strategy to generate fixed-size features without being affected by image scale. SSD [8] achieves end-to-end object detection by simultaneously predicting the position and category of targets on different levels of feature maps. However, these methods can lead to misalignment and background interference between the target and candidate regions. In order to solve the problem, a two-stage detector achieves alignment between the target and the candidate region through multiple fine refinements of the candidate regions. All candidate boxes generated by RPN (including positive samples and the background) would perform nonmaximum suppression to remove highly repetitive boxes, resulting in the final set of proposal boxes. The Roi transformer [2] improves the accuracy of target localization by finely adjustingthe internal features of candidate boxes, thereby eliminating background interference. [PERSON] [1] designed a new multi-category rotation detector, SCRNet, for small, dense, and rotating objects, which integrates multi-layer features into anchor sampling to improve sensitivity for detecting small targets. However, such techniques often necessitate intricate calculations to eliminate redundant and overlapping candidate boxes, posing challenges in portability across distinct detectors and leading to increased inference times, rendering them unsuitable for embedded platforms with limited computational capabilities. The parameters and structure of these static networks mentioned above are fixed during inference and are not suitable for aerial targets with high aspect ratios and arbitrary directional orientations. At present, research on RS object detection has undergone a transformation from static networks to dynamic networks. Specifically, the structure or parameters of dynamic networks can adapt to different inputs and have significant advantages in accuracy, computational efficiency, and adaptability. CAT [9] is a recursive neural network model that adaptively calculates time, allowing the model to dynamically adjust the calculation time when processing sequence data to more effectively adapt to inputs of different step sizes. However, in some cases, the generalization ability of this method may be limited for different tasks, especially on test data with significant differences from the train data. [PERSON] [10] proposed a neural network structure called the Sparsely-Gated Mixture-of-Experts layer (MoE), which can support a large model scale and achieve efficient training and inference through effective gating mechanisms. However, the model structure is relatively complex, occupying a large amount of storage space, and the training and inference time would increase. In summary, the previous dynamic network had high computational complexity, making it difficult to deploy and apply to various detectors. Specifically, aerial targets such as planes and ships often have a high aspect ratio and are arranged in multiple directions. Existing standard convolution structures slide horizontally, which can lead to angular differences with aerial targets.