Swarm control has become a challenging topic for the current unmanned aerial vehicle (UAV) swarm due to its conflicting individual behaviors and high external interference. However, in contrast to static obstacles, limited attention has been paid to the fission–fusion behavior of the swarm against dynamic obstacles. In this paper, inspired by the interaction mechanism and fission–fusion motion of starlings, we propose a Bio-inspired Self-organized Fission–fusion Control (BiSoFC) algorithm for the UAV swarm, where the number of UAVs in the sub-swarm is controllable. It solves the problem of swarm control under dynamic obstacle interference with the tracking function. Firstly, we establish the kinematic equations of the individual UAV and swarm controllers and introduce a fission–fusion control framework to achieve the fission–fusion movement of the UAV swarm with a lower communication load. Afterward, a sub-swarm selection algorithm is built upon the topological interaction structure. When a swarm is faced with different tasks, the swarm that can control the number of agents in a sub-swarm can accomplish the corresponding task with a more reasonable number of agents. Finally, we design a sub-swarm trapping algorithm with a tracking function for the dynamic obstacles. The simulation results show that the UAV swarm can self-organize fission sub-swarms to cope with dynamic obstacles under different disturbance situations, and successfully achieve the goal of protecting the parent swarm from dynamic obstacles. The experimental results prove the feasibility and effectiveness of our proposed control algorithm.

3D reconstruction of Unmanned Aerial Vehicle (UAV) scenes is vital for agriculture, environmental protection, urban planning, and disaster response, to name a few. However, data acquisition can be constrained and hazardous under hostile environments, which limits the image data available in real-world applications. In this work, we propose a text-driven online editing framework for UAV scenes, which can generate novel views of existing scenes with abundant editing types. Compared with small single-object scenes, large-scale UAV scene editing suffers from several particular challenges: 1) broader capturing scope exhibits illumination variation and complicated objects that reduce the 3D scene consistency after editing; and 2) high-resolution 2D editing and 3D reconstruction can be computationally expensive with tremendous GPU memory. To tackle these issues, we first design a dual-branch compact NeRF structure to reduce memory usage and enhance accuracy for 3D reconstruction. We then introduce a sub-pixel sampling scheme to expedite the generation of low-resolution images for 2D editing, followed by a super-resolution module that restores the fine details of rendered images. Additionally, we develop a grouped content filtering mechanism to improve the 3D scene consistency of the model by matching the rendering images and text descriptions, which also significantly reduces memory usage during editing. Extensive experiments demonstrate that the proposed method can achieve various editing effects, including different seasons, weather conditions, times of the day, disaster scenarios, etc. Our technique is computationally efficient and conveniently expandable for large-scale UAV scenes, alleviating data scarcity in harsh scenarios.

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).

Cross-view object localization (CVOGL) offers fine-grained geographic information regarding the specified object of interest, in comparison to typical cross-view geo-location at the image level. However, it faces more challenges, primarily due to significant visual appearance changes induced by viewpoint discrepancies and the difficulty of accurately identifying corresponding objects within reference images containing multiple targets. To address these challenges, we propose a novel CVOGL model termed HALO. First, we introduce a cascade cross-view feature interaction module that enables effective local-to-global feature fusion across different viewpoints, thereby enhancing the feature representation of objects in the reference image. Second, to mitigate the scale variations and feature distribution discrepancies between the query and reference images, we propose an adaptive feature hierarchization and aggregate module. It extracts more representative global descriptors by adaptively hierarchizing and aggregating features. Lastly, utilizing the learned global descriptors, we perform cross-view CL and introduce a hard sample mining strategy to further enhance the discriminative ability of the network. Through these advancements, we significantly improve the discriminative representations of the query objects at both views. Extensive experiments on the CVOGL dataset demonstrate the effectiveness and robustness of the proposed method. In the CVOGL task of drone→satellite, HALO improves 2.36% in acc@0.5 on the test sets. Similarly, HALO shows an increase of 2.49% in acc@0.5 on the validation set for the CVOGL task of ground→satellite. Our codes are publicly available at https://github.com/ZehaoZhang-Uestc/HALO.