Current state-of-the-art two-stage detectors generate oriented proposals through time-
consuming schemes. This diminishes the detectors' speed, thereby becoming the …

Recently, object detection in aerial images has gained much attention in computer vision.
Different from objects in natural images, aerial objects are often distributed with arbitrary …

Existing rotated object detectors are mostly inherited from the horizontal detection paradigm,
as the latter has evolved into a well-developed area. However, these detectors are difficult to …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

Boundary discontinuity and its inconsistency to the final detection metric have been the
bottleneck for rotating detection regression loss design. In this paper, we propose a novel …

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 …

The development of detection methods for oriented object detection remains a challenging
task. A considerable obstacle is the wide variation in the shape (eg, aspect ratio) of objects …

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 identification of small land targets in remote sensing imagery has emerged as a
significant research objective. Despite significant advancements in object detection …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …