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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
Most implemented papers
The KFIoU Loss for Rotated Object Detection
yangxue0827/RotationDetection
•
•
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
xView: Objects in Context in Overhead Imagery
We introduce a new large-scale dataset for the advancement of object detection techniques and overhead object detection research.
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall accuracy of ~95%.
Learning RoI Transformer for Oriented Object Detection in Aerial Images
dingjiansw101/AerialDetection
•
•
CVPR 2019
Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects.
Learning Modulated Loss for Rotated Object Detection
Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) to describe the rotated bounding box and l1-loss as the loss function.
Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
ming71/DAL
•
•
With the newly introduced DAL, we achieve superior detection performance for arbitrary-oriented objects with only a few horizontal preset anchors.
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
yangxue0827/RotationDetection
•
•
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
dingjiansw101/AerialDetection
•
•
In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI.
Oriented RepPoints for Aerial Object Detection
LiWentomng/OrientedRepPoints
•
•
CVPR 2022
In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings.
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
yangxue0827/RotationDetection
•
•
NeurIPS 2021
Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection.
