Detecting small or tiny objects is always a difficult and challenging issue in computer vision.
In this paper, we provide a latest and comprehensive survey of deep learning-based …

Oriented object detection is a practical and challenging task in remote sensing image
interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to …

Object detection has made tremendous progress in natural images over the last decade.
However, the results are hardly satisfactory when the natural image object detection …

Detecting objects in remote sensing images (RSIs) using oriented bounding boxes (OBBs) is
flourishing but challenging, wherein the design of OBB representations is the key to …

Few-shot aerial image object detection aims to rapidly detect object instances of novel
category in aerial images by using few labeled samples. However, due to the complex …

Oriented object detection (OOD) aims to precisely detect the objects with arbitrary orientation
in remote sensing images (RSIs). Up to now, most of the bounding box regression (BBR) …

Practical applications ask for object detection models that achieve high performance at low
overhead. Knowledge distillation demonstrates favorable potential in this case by …

Deep neural networks (DNNs) have found widespread applications in interpreting remote
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …

Fine-grained oriented object recognition (FGO) is a practical need for intellectually
interpreting remote sensing images. It aims at realizing fine-grained classification and …

Current mainstream object detection methods for large aerial images usually divide large
images into patches and then exhaustively detect the objects of interest on all patches, no …