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computer-vision

Object Detection In Aerial Images

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in object-detection-in-aerial-images

Trend

Dataset

Best Model

Actions

DOTA

DOTA

HRSC2016

HRSC2016

DIOR-R

DIOR-R

Libraries

i

Use these libraries to find object-detection-in-aerial-images models and implementations

open-mmlab/mmrotate

11 papers 1,886

Datasets

DOTA

xView

iSAID

AID

DIOR

HRSC2016

Subtasks

No subtasks available.

Most implemented papers

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

Liangpei Zhang, Gui-Song Xia, Serge J. Belongie, X. Bai, Jian Ding, Zhen Zhu, Jiebo Luo, M. Datcu, M. Pelillo•Mon Nov 27 2017

A large-scale Dataset for Object deTection in Aerial images (DOTA) is introduced and state-of-the-art object detection algorithms on DOTA are evaluated, demonstrating that DOTA well represents real Earth Vision applications and are quite challenging.

2527

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

DIOR

DIOR

xView

xView

FAIR1M-2.0

FAIR1M-2.0

DOTA 1.0

DOTA 1.0

yangxue0827/RotationDetection

6 papers 1,080

zcablii/Large-Selective-Kernel-Netw…

4 papers 488

3 papers 488

PaddlePaddle/PaddleDetection

2 papers 12,867

2 papers 626

LandCover.ai

LandCover.ai

DOTA 2.0

SODA-A

C2A: Human Detection in Disaster Scenarios

C2A: Human Detection in Disaster Scenarios

0

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

Jin Tang, Xue Yang, Junchi Yan, Xiaokang Yang, Wenlong Liao, Tao He•Mon Apr 27 2020

Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects and a novel IoU constant factor is added to the smooth L1 loss to address the long standing boundary problem.

326 0

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

Kai Chen, Wenwei Zhang, Chengqi Lyu, Haian Huang, Yue Zhou, Yudong Wang, Yanyi Liu, Shilong Zhang•Tue Dec 13 2022

An efficient real-time object detector is designed that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection and introduces soft labels when calculating matching costs in the dynamic label assignment to improve accuracy.

674 0

On the Arbitrary-Oriented Object Detection: Classification Based Approaches Revisited

Xue Yang, Junchi Yan, Tao He•Wed Mar 11 2020

A Circular Smooth Label technique is devised to handle the periodicity of angle and increase the error tolerance to adjacent angles and an object heading detection module is developed, which can be useful when the exact heading orientation information is needed e.g. for ship and plane heading detection.

171 0

Oriented R-CNN for Object Detection

Junwei Han, Xiwen Yao, Gong Cheng, Xingxing Xie, Jiabao Wang•Wed Aug 11 2021

This work proposes an effective and simple oriented object detection framework, termed Oriented R-CNN, which is a general two-stage oriented detector with promising accuracy and efficiency, and an oriented Region Proposal Network (oriented RPN) that directly generates high-quality oriented proposals in a nearly cost-free manner.

984 0

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

Xian Sun, Kun Fu, Xue Yang, Junchi Yan, Jirui Yang, Yue Zhang, Tengfei Zhang, Zhi Guo•Fri Nov 16 2018

Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This paper presents a novel multi-category rotation detector for small, cluttered and rotated objects, namely SCRDet. Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects. Meanwhile, the supervised pixel attention network and the channel attention network are jointly explored for small and cluttered object detection by suppressing the noise and highlighting the objects feature. For more accurate rotation estimation, the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the state-of-the-art performance of our detector. The code and models will be available at https://github.com/DetectionTeamUCAS.

842 0

ReDet: A Rotation-equivariant Detector for Aerial Object Detection

Guisong Xia, Jian Ding, Nan Xue, Jiaming Han•Fri Mar 12 2021

A Rotation-equivariant Detector (ReDet) is proposed, which explicitly encodes rotation equivariance and rotation invariance and incorporates 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.

720 0

Align Deep Features for Oriented Object Detection

Guisong Xia, Jian Ding, Jie Li, Jiaming Han•Thu Aug 20 2020

A single-shot alignment network (S2A-Net) consisting of two modules: a feature alignment module (FAM) and an oriented detection module (ODM) that can achieve the state-of-the-art performance on two commonly used aerial objects’ data sets while keeping high efficiency.

974 0

Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

Xue Yang, Junchi Yan, Liping Hou, Yue Zhou, Wentao Wang•Wed Nov 18 2020

This paper explores a relatively less-studied methodology based on classification for rotation detection, and proposes new techniques to inherently dismiss the boundary discontinuity issue as encountered by the regression-based detectors.

282 0

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

Xue Yang, Junchi Yan, Qingqing Liu, Ang Li•Wed Aug 14 2019

This paper proposes an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity and designs a feature refinement module to improve detection performance by getting more accurate features.

1062 0

Adding a benchmark result helps the community track progress.

Object Detection In Aerial Images | State-of-the-Art