Deep learning-based detection from the perspective of small or tiny objects: A survey
K Tong, Y Wu - Image and Vision Computing, 2022 - Elsevier
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 …
In this paper, we provide a latest and comprehensive survey of deep learning-based …
Anchor-free oriented proposal generator for object detection
Oriented object detection is a practical and challenging task in remote sensing image
interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to …
interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to …
Swin-transformer-enabled YOLOv5 with attention mechanism for small object detection on satellite images
H Gong, T Mu, Q Li, H Dai, C Li, Z He, W Wang, F Han… - Remote Sensing, 2022 - mdpi.com
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 …
However, the results are hardly satisfactory when the natural image object detection …
On improving bounding box representations for oriented 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 …
flourishing but challenging, wherein the design of OBB representations is the key to …
Robust few-shot aerial image object detection via unbiased proposals filtration
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 …
category in aerial images by using few labeled samples. However, due to the complex …
Building a bridge of bounding box regression between oriented and horizontal object detection in remote sensing images
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) …
in remote sensing images (RSIs). Up to now, most of the bounding box regression (BBR) …
Instance-aware distillation for efficient object detection in remote sensing images
Practical applications ask for object detection models that achieve high performance at low
overhead. Knowledge distillation demonstrates favorable potential in this case by …
overhead. Knowledge distillation demonstrates favorable potential in this case by …
A comprehensive study on the robustness of deep learning-based image classification and object detection in remote sensing: Surveying and benchmarking
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 …
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …
SFRNet: Fine-grained oriented object recognition via separate feature refinement
Fine-grained oriented object recognition (FGO) is a practical need for intellectually
interpreting remote sensing images. It aims at realizing fine-grained classification and …
interpreting remote sensing images. It aims at realizing fine-grained classification and …
Fewer is more: Efficient object detection in large aerial images
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 …
images into patches and then exhaustively detect the objects of interest on all patches, no …