Object Detection In Aerial Images

48 papers with code • 3 benchmarks • 7 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 )


Use these libraries to find Object Detection In Aerial Images models and implementations

Most implemented papers

R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

yangxue0827/RotationDetection 15 Aug 2019

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.

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

PaddlePaddle/PaddleDetection CVPR 2018

The fully annotated DOTA images contains $188, 282$ instances, each of which is labeled by an arbitrary (8 d. o. f.)

RTMDet: An Empirical Study of Designing Real-Time Object Detectors

open-mmlab/mmdetection 14 Dec 2022

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.

SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

SJTU-Thinklab-Det/DOTA-DOAI 28 Apr 2020

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 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.

SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects

DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow 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 21 Aug 2020

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 CVPR 2021

Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc.

ReDet: A Rotation-equivariant Detector for Aerial Object Detection

csuhan/ReDet CVPR 2021

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 ICCV 2021

Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes.