# Learning a Rotation Invariant Detector with Rotatable Bounding Box

arXiv: https://arxiv.org/abs/1711.09405 · HTML: https://mlsanity.com/paper/1711.09405/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2017.

## Built on by (most cited first, 21 of 21)

- [ReDet: A Rotation-equivariant Detector for Aerial Object Detection](https://mlsanity.com/paper/2103.07733/) (2021)
- [AO2-DETR: Arbitrary-Oriented Object Detection Transformer](https://mlsanity.com/paper/2205.12785/) (2022)
- [RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection](https://mlsanity.com/paper/2109.11906/) (2021)
- [FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection](https://mlsanity.com/paper/2111.10780/) (2021)
- [Learning Calibrated-Guidance for Object Detection in Aerial Images](https://mlsanity.com/paper/2103.11399/) (2021)
- [TS4Net: Two-Stage Sample Selective Strategy for Rotating Object Detection](https://mlsanity.com/paper/2108.03116/) (2021)
- [Sampling Equivariant Self-attention Networks for Object Detection in Aerial Images](https://mlsanity.com/paper/2111.03420/) (2021)
- [Point RCNN: An Angle-Free Framework for Rotated Object Detection](https://mlsanity.com/paper/2205.14328/) (2022)
- [DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection](https://mlsanity.com/paper/2109.06148/) (2021)
- [Towards Rotation Invariance in Object Detection](https://mlsanity.com/paper/2109.13488/) (2021)
- [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)
- [Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2404.18426/) (2024)
- [A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application](https://mlsanity.com/paper/2101.07383/) (2021)
- [A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer](https://mlsanity.com/paper/2106.01927/) (2021)
- [Anchor Free remote sensing detector based on solving discrete polar coordinate equation](https://mlsanity.com/paper/2303.11694/) (2023)
- [An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single Camera](https://mlsanity.com/paper/2309.08369/) (2023)
- [Edge Wasserstein Distance Loss for Oriented Object Detection](https://mlsanity.com/paper/2312.07048/) (2023)
- [Efficient Oriented Object Detection with Enhanced Small Object Recognition in Aerial Images](https://mlsanity.com/paper/2412.12562/) (2024)
- [Benchmarking pig detection and tracking under diverse and challenging conditions](https://mlsanity.com/paper/2507.16639/) (2025)
- [RMK RetinaNet: Rotated Multi-Kernel RetinaNet for Robust Oriented Object Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2603.04793/) (2026)

## Abstract (arXiv)

Detection of arbitrarily rotated objects is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. The existing methods are not robust to angle varies of the objects because of the use of traditional bounding box, which is a rotation variant structure for locating rotated objects. In this article, a new detection method is proposed which applies the newly defined rotatable bounding box (RBox). The proposed detector (DRBox) can effectively handle the situation where the orientation angles of the objects are arbitrary. The training of DRBox forces the detection networks to learn the correct orientation angle of the objects, so that the rotation invariant property can be achieved. DRBox is tested to detect vehicles, ships and airplanes on satellite images, compared with Faster R-CNN and SSD, which are chosen as the benchmark of the traditional bounding box based methods. The results shows that DRBox performs much better than traditional bounding box based methods do on the given tasks, and is more robust against rotation of input image and target objects. Besides, results show that DRBox correctly outputs the orientation angles of the objects, which is very useful for locating multi-angle objects efficiently. The code and models are available at https://github.com/liulei01/DRBox.