# PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images

arXiv: https://arxiv.org/abs/2010.08720 · HTML: https://mlsanity.com/paper/2010.08720/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss](https://mlsanity.com/paper/2101.11952/) (2021)
- [Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence](https://mlsanity.com/paper/2106.01883/) (2021)
- [The KFIoU Loss for Rotated Object Detection](https://mlsanity.com/paper/2201.12558/) (2022)
- [Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection](https://mlsanity.com/paper/2203.15221/) (2022)
- [Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization](https://mlsanity.com/paper/2209.10839/) (2022)
- [PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond](https://mlsanity.com/paper/2105.02184/) (2021)
- [G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection](https://mlsanity.com/paper/2205.11796/) (2022)
- [FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection](https://mlsanity.com/paper/2111.10780/) (2021)
- [Turning a CLIP Model into a Scene Text Spotter](https://mlsanity.com/paper/2308.10408/) (2023)
- [Theoretically Achieving Continuous Representation of Oriented Bounding Boxes](https://mlsanity.com/paper/2402.18975/) (2024)
- [Oriented object detection in optical remote sensing images using deep learning: a survey](https://mlsanity.com/paper/2302.10473/) (2023)
- [DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images](https://mlsanity.com/paper/2110.01025/) (2021)
- [EAutoDet: Efficient Architecture Search for Object Detection](https://mlsanity.com/paper/2203.10747/) (2022)
- [Learning Remote Sensing Object Detection with Single Point Supervision](https://mlsanity.com/paper/2305.14141/) (2023)
- [Multi-Grained Angle Representation for Remote Sensing Object Detection](https://mlsanity.com/paper/2209.02884/) (2022)
- [Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images](https://mlsanity.com/paper/2103.13151/) (2021)
- [Linear Gaussian Bounding Box Representation and Ring-Shaped Rotated Convolution for Oriented Object Detection](https://mlsanity.com/paper/2311.05410/) (2023)
- [OSKDet: Towards Orientation-sensitive Keypoint Localization for Rotated Object Detection](https://mlsanity.com/paper/2104.08697/) (2021)
- [Oriented Object Detection in Aerial Images Based on Area Ratio of Parallelogram](https://mlsanity.com/paper/2109.10187/) (2021)
- [Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images](https://mlsanity.com/paper/2209.02200/) (2022)
- [Anchor Free remote sensing detector based on solving discrete polar coordinate equation](https://mlsanity.com/paper/2303.11694/) (2023)
- [Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection](https://mlsanity.com/paper/2306.16718/) (2023)
- [FPDIoU Loss: A Loss Function for Efficient Bounding Box Regression of Rotated Object Detection](https://mlsanity.com/paper/2405.09942/) (2024)
- [InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2405.11293/) (2024)
- [Enhancing Rotated Object Detection via Anisotropic Gaussian Bounding Box and Bhattacharyya Distance](https://mlsanity.com/paper/2510.16445/) (2025)

## Abstract (arXiv)

Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we represent the oriented objects by polar method in polar coordinate and propose PolarDet, a fast and accurate one-stage object detector based on that representation. Our detector introduces a sub-pixel center semantic structure to further improve classifying veracity. PolarDet achieves nearly all SOTA performance in aerial object detection tasks with faster inference speed. In detail, our approach obtains the SOTA results on DOTA, UCAS-AOD, HRSC with 76.64% mAP, 97.01% mAP, and 90.46% mAP respectively. Most noticeably, our PolarDet gets the best performance and reaches the fastest speed(32fps) at the UCAS-AOD dataset.