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
PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector
With multi-scale training and testing, PP-YOLOE-R-l and PP-YOLOE-R-x further improve the detection precision to 80. 02 and 80. 73 mAP.
Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images
Shank2358/GGHL
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Specifically, sampling positions of the localization convolution in TS-Conv are supervised by the oriented bounding box (OBB) prediction associated with spatial coordinates, while sampling positions and convolutional kernel of the classification convolution are designed to be adaptively adjusted according to different orientations for improving the orientation robustness of features.
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalability and representation ability.
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels.
An Empirical Study of Remote Sensing Pretraining
To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.
Learning to Reduce Information Bottleneck for Object Detection in Aerial Images
ssyc123/gsnet
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In this letter, we first underline the importance of the neck network in object detection from the perspective of information bottleneck.
The KFIoU Loss for Rotated Object Detection
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.
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
qxgeng/dea-net
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On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0. 40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3. 08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone.
DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images
zf020114/dardet
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Rotated object detection in aerial images has received increasing attention for a wide range of applications.
A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection
Shank2358/GGHL
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Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2-D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects.
