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
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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
YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images
2) Small object size leads to insufficient information for effective detection.
Robust Tiny Object Detection in Aerial Images amidst Label Noise
In this study, we address the intricate issue of tiny object detection under noisy label supervision.
CLIP-guided Source-free Object Detection in Aerial Images
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions.
ABFL: Angular Boundary Discontinuity Free Loss for Arbitrary Oriented Object Detection in Aerial Images
Existing methods lack intuitive modeling of angle difference measurement in oriented Bbox representations.
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
In this paper, we aim to develop open-vocabulary object detection (OVD) technique in aerial images that scales up object vocabulary size beyond training data.
Object Detection in Aerial Images in Scarce Data Regimes
We demonstrate this with an in-depth analysis of existing FSOD methods on aerial images and observed a large performance gap compared to natural images.
A Billion-scale Foundation Model for Remote Sensing Images
Recently, research in the remote sensing field has focused primarily on the pretraining method and the size of the dataset, with limited emphasis on the number of model parameters.
Aerial Image Object Detection With Vision Transformer Detector (ViTDet)
Our results show that ViTDet can consistently outperform its convolutional neural network counterparts on horizontal bounding box (HBB) object detection by a large margin (up to 17% on average precision) and that it achieves the competitive performance for oriented bounding box (OBB) object detection.
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
Then, we propose a Translation-Scale-Rotation Alignment (TSRA) module to address the problem by calibrating the feature maps from these two modalities.
Object Detection in Aerial Images with Uncertainty-Aware Graph Network
To achieve this, we first detect objects and then measure their semantic and spatial distances to construct an object graph, which is then represented by a graph neural network (GNN) for refining visual CNN features for objects.
