Convolutional neural networks (CNNs) are one of the main types of neural networks used for
image recognition and classification. CNNs have several uses, some of which are object …

Deep learning-based object detection solutions emerged from computer vision has
captivated full attention in recent years. The growing UAV market trends and interest in …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

UAV multitarget detection plays a pivotal role in civil and military fields. Although deep
learning methods provide a more effective solution to this task, changes in target size, shape …

In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary
orientations having the cluttered surroundings. Unlike the mainstreamed approaches …

The past decade has witnessed significant progress on detecting objects in aerial images
that are often distributed with large-scale variations and arbitrary orientations. However …

In he past decade, object detection has achieved significant progress in natural images but
not in aerial images, due to the massive variations in the scale and orientation of objects …

The prevailing applications of Unmanned Aerial Vehicles (UAVs) in transportation systems
promote the development of object detection methods to collect real-time traffic information …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Benefiting from the development of convolutional neural networks (CNNs), many excellent
algorithms for object detection have been presented. Remote sensing object detection …