In recent years, unmanned aerial vehicles (UAVs) have shown significant potential across diverse applications, drawing attention from both academia and industry. In specific scenarios, UAVs are expected to achieve formation flying without relying on communication or external assistance. In this context, our work focuses on the classic leader-follower formation and presents a learning-based UAV chasing control method that enables a quadrotor UAV to autonomously chase a highly maneuverable fixed-wing UAV. The proposed method utilizes a neural network called Vision Follow Net (VFNet), which integrates monocular visual data with the UAV’s flight state information. Utilizing a multi-head self-attention mechanism, VFNet aggregates data over a time window to predict the waypoints for the chasing flight. The quadrotor’s yaw angle is controlled by calculating the line-of-sight (LOS) angle to the target, ensuring that the target remains within the onboard camera’s field of view during the flight. A simulation flight system is developed and used for neural network training and validation. Experimental results indicate that the quadrotor maintains stable chasing performance through various maneuvers of the fixed-wing UAV and can sustain formation over long durations. Our research explores the use of end-to-end neural networks for UAV formation flying, spanning from perception to control.

Deploying high-accuracy detection models on lightweight edge devices (e.g., drones) is challenging due to hardware constraints. To achieve satisfactory detection results, a common solution is to compress and transmit the images to a cloud server where powerful models can be used. However, the image compression process for transmission may lead to a reduction in detection accuracy. In this paper, we propose a feature compression method tailored for object detection tasks, and it can be easily integrated with existing learned image compression models. In the method, the encoding process consists of two steps. Firstly, we use a feature extractor to obtain the low-level feature, and then use a mask generator to obtain an object mask to select regions containing objects. Secondly, we use a neural network encoder to compress the masked feature. As for decoding, a neural network decoder is used to restore the compressed representation into the feature that can be directly inputted into the object detection model. The experimental results demonstrate that our method surpasses existing compression techniques. Specifically, when compared to one of the leading methods—TCM2023—our approach achieves a 25.3% reduction in compressed file size and a 6.9% increase in mAP0.5.