Object Detection In Aerial Images
53 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
FlightScope: A Deep Comprehensive Assessment of Aircraft Detection Algorithms in Satellite Imagery
Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical, and environmental monitoring.
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
On the Robustness of Object Detection Models in Aerial Images
The robustness of object detection models is a major concern when applied to real-world scenarios.
Spatial Transform Decoupling for Oriented Object Detection
yuhongtian17/spatial-transform-decoupling
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Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks.
Density Crop-guided Semi-supervised Object Detection in Aerial Images
One of the important bottlenecks in training modern object detectors is the need for labeled images where bounding box annotations have to be produced for each object present in the image.
A Robust Feature Downsampling Module for Remote Sensing Visual Tasks
lwCVer/RFD
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IEEE Transactions on Geoscience and Remote Sensing 2023
To address this problem, we propose a new and universal downsampling module named robust feature downsampling (RFD).
Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
The Detection Transformer (DETR) has emerged as a pivotal role in object detection tasks, setting new performance benchmarks due to its end-to-end design and scalability.
Large Selective Kernel Network for Remote Sensing Object Detection
zcablii/Large-Selective-Kernel-Network
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ICCV 2023
To the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection.
Adaptive Rotated Convolution for Rotated Object Detection
In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image.
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection.
