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
Most implemented papers
Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection
SDL-GuoZonghao/BeyondBoundingBox
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CVPR 2021
Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects.
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.
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.
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.
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
dingjiansw101/RoITransformer_DOTA
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Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects.
Clustered Object Detection in Aerial Images
fyangneil/Clustered-Object-Detection-in-Aerial-Image
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ICCV 2019
The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet).
Gliding vertex on the horizontal bounding box for multi-oriented object detection
Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts.
Density Map Guided Object Detection in Aerial Images
Cli98/DMNet
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Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.
LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery
MortenTabaka/Semantic-segmentation-of-LandCover.ai-dataset
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km with resolution 50 cm per pixel and 176. 76 sq.
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
Anymake/DRN_CVPR2020
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CVPR 2020
However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.
