Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature extraction in OOD becomes more challenging due to the diversity of object orientations. In this paper, we propose a semantic-driven rotational feature enhancement method, termed SREDet, to fully leverage the joint semantic and spatial information of oriented objects in the remote sensing images. We first construct a multi-rotation feature pyramid network (MRFPN), which leverages a fusion of multi-angle and multiscale feature maps to enhance the capability to extract features from different orientations. Then, considering feature confusion and contamination caused by the dense arrangement of objects and background interference, we present a semantic-driven feature enhancement module (SFEM), which decouples features in the spatial domain to separately enhance the features of objects and weaken those of backgrounds. Furthermore, we introduce an error source evaluation metric for rotated object detection to further analyze detection errors and indicate the effectiveness of our method. Extensive experiments demonstrate that our SREDet method achieves superior performance on two commonly used remote sensing object detection datasets (i.e., DOTA and HRSC2016).

The motion of unmanned aerial vehicle (UAV) swarms is a complex research area due to the involvement of various system components, including perception, control, and decision‐making policies. However, compared to static flight behaviors, the fission–fusion motion of UAV swarms in response to multiple unknown dynamic disturbances has received relatively less attention. This paper proposes a reinforcement learning–based UAV swarm fission–fusion approach with real‐world data integrated validation for the swarm’s fission‐fusion behavior in response to multiple unknown dynamic disturbances, along with a system validation method utilizing real‐world data. The proposed approach effectively integrates fission–fusion dynamics with perception and control to enable UAV swarms to function robustly in the presence of such disturbances. First, we develop a self‐organized control framework that facilitates the coordinated motion of multiple UAV swarms. Second, we introduce a reinforcement learning–based fission–fusion confrontation algorithm designed to minimize resource consumption while effectively responding to multiple unknown dynamic disturbances. Finally, we present a real‐world data‐based validation system based on AirSim, which allows comprehensive evaluation of UAV swarm performance in actual environments. Simulation results demonstrate that when UAV swarms operate in environments with multiple unknown disturbances, they can successfully perform self‐organized fission‐fusion motion, effectively protecting the parent‐swarm from the impact of multiple unknown dynamic disturbances.