Navigating drones through natural language commands remains challenging due to the
dearth of accessible multi-modal datasets and the stringent precision requirements for …

In aerial images captured by drones or satellite remote sensing images, object information is
weak and difficult to distinguish from the background, with significant variations in object …

The increasing use of unmanned aerial vehicle (UAV) devices in diverse fields such as
agriculture, surveillance, and aerial photography has led to a significant demand for …

Local feature matching enjoys wide-ranging applications in the realm of computer vision,
encompassing domains such as image retrieval, 3D reconstruction, and object recognition …

The various sensing technologies such as cameras LiDAR radar and satellites with
advanced machine learning models offers a comprehensive approach to environmental …

Object detection on aerial images captured by Unmanned Aerial Vehicles (UAVs) has a
wide range of applications. Due to the variations in illumination, weather conditions and …

Knowledge distillation (KD) is an effective method for compressing models in object
detection tasks. Due to limited computational capability, unmanned aerial vehicle-based …

Emerging Artificial Intelligence of Things (AIoT) applications desire online prediction using
deep neural network (DNN) models on mobile devices. However, due to the movement of …

When deploying unmanned aerial vehicle (UAV) object detection networks to complex, real-
world scenes, generalization ability is often reduced due to domain shift. While most existing …

It is difficult for object detection networks to effectively pay attention to the characteristics of
small objects, especially when edge computing devices such as drones process their aerial …