Object Detection In Aerial Images
54 papers with code • 6 benchmarks • 8 datasets
Object Detection in Aerial Images is the task of detecting objects from aerial images.
( Image credit: DOTA: A Large-Scale Dataset for Object Detection in Aerial Images )
Benchmarks
These leaderboards are used to track progress in Object Detection In Aerial Images
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Libraries
Use these libraries to find Object Detection In Aerial Images models and implementations
DOTA
xView
iSAID
AID
HRSC2016
LandCover.ai
DOTA 2.0
SODA-A
Latest papers with no code
MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images
Thus, the Gaussian Angle Loss (GA Loss) is presented to solve this problem by adding a corrected loss for square targets.
Point RCNN: An Angle-Free Framework for Rotated Object Detection
To tackle this problem, we propose a purely angle-free framework for rotated object detection, called Point RCNN, which mainly consists of PointRPN and PointReg.
Rotated Object Detection via Scale-invariant Mahalanobis Distance in Aerial Images
The eight-parameter (coordinates of box vectors) methods in rotated object detection usually use ln-norm losses (L1 loss, L2 loss, and smooth L1 loss) as loss functions.
Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection
Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost.
Focus-and-Detect: A Small Object Detection Framework for Aerial Images
Despite recent advances, object detection in aerial images is still a challenging task.
EAutoDet: Efficient Architecture Search for Object Detection
In contrast, this paper introduces an efficient framework, named EAutoDet, that can discover practical backbone and FPN architectures for object detection in 1. 4 GPU-days.
Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images
The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for various computer vision tasks has paved the path for numerous opportunities to make them more effective and valuable.
Investigating the Challenges of Class Imbalance and Scale Variation in Object Detection in Aerial Images
While object detection is a common problem in computer vision, it is even more challenging when dealing with aerial satellite images.
Object Detection in Aerial Images: What Improves the Accuracy?
In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images.
Sampling Equivariant Self-attention Networks for Object Detection in Aerial Images
Sampling equivariant networks can adjust sampling from input feature maps according to the transformation of the object, allowing a kernel to extract features of an object under different transformations.
