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
Most implemented papers
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
yangxue0827/RotationDetection
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Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features.
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.
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
PaddlePaddle/PaddleDetection
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CVPR 2018
The fully annotated DOTA images contains $188, 282$ instances, each of which is labeled by an arbitrary (8 d. o. f.)
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
SJTU-Thinklab-Det/DOTA-DOAI
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Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited
yangxue0827/RotationDetection
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ECCV 2020
For the resulting circularly distributed angle classification problem, we first devise a Circular Smooth Label technique to handle the periodicity of angle and increase the error tolerance to adjacent angles.
ReDet: A Rotation-equivariant Detector for Aerial Object Detection
More precisely, we incorporate rotation-equivariant networks into the detector to extract rotation-equivariant features, which can accurately predict the orientation and lead to a huge reduction of model size.
Oriented R-CNN for Object Detection
jbwang1997/OBBDetection
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ICCV 2021
Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes.
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow
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ICCV 2019
Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects.
Align Deep Features for Oriented Object Detection
csuhan/s2anet
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However most of existing methods rely on heuristically defined anchors with different scales, angles and aspect ratios and usually suffer from severe misalignment between anchor boxes and axis-aligned convolutional features, which leads to the common inconsistency between the classification score and localization accuracy.
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
yangxue0827/RotationDetection
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CVPR 2021
Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc.
