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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds.
Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
yijingru/BBAVectors-Oriented-Object-Detection
•
•
To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task.
Single-Stage Rotation-Decoupled Detector for Oriented Object
Capino512/pytorch-rotation-decoupled-detector
•
•
Oriented object detection has received extensive attention in recent years, especially for the task of detecting targets in aerial imagery.
Tiny Object Detection in Aerial Images
To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset.
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images
ming71/CFC-Net
•
•
The proposed framework creates more powerful semantic representations for objects in remote sensing images and achieves high-performance real-time object detection.
Learning Calibrated-Guidance for Object Detection in Aerial Images
WeiZongqi/CG-Net
•
•
Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.
Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss
ming71/RIDet
•
•
In this paper, we propose a Representation Invariance Loss (RIL) to optimize the bounding box regression for the rotating objects.
TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection
We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues ($i. e.,$ heatmap) instead of oriented box offsets regression.
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
This training strategy encourages the network to adapt to new classes as it would at test time.
A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection
Shank2358/GGHL
•
•
Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2-D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects.
