# Measuring the Impact of Rotation Equivariance on Aerial Object Detection

Source: [Measuring the Impact of Rotation Equivariance on Aerial Object Detection](<https://www.sota2.com/research/paper/68fa79f81e209131cea80573>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:09:30.174Z

Authors: Xiuyu Wu, Xinhao Wang, Xiubin Zhu, Lan Yang, Jiyuan Liu, Xingchen Hu

Published: 2025-07-14T04:04:23.000Z

## Abstract

Due to the arbitrary orientation of objects in aerial images, rotation equivariance is a critical property for aerial object detectors. However, recent studies on rotation-equivariant aerial object detection remain scarce. Most detectors rely on data augmentation to enable models to learn approximately rotation-equivariant features. A few detectors have constructed rotation-equivariant networks, but due to the breaking of strict rotation equivariance by typical downsampling processes, these networks only achieve approximately rotation-equivariant backbones. Whether strict rotation equivariance is necessary for aerial image object detection remains an open question. In this paper, we implement a strictly rotation-equivariant backbone and neck network with a more advanced network structure and compare it with approximately rotation-equivariant networks to quantitatively measure the impact of rotation equivariance on the performance of aerial image detectors. Additionally, leveraging the inherently grouped nature of rotation-equivariant features, we propose a multi-branch head network that reduces the parameter count while improving detection accuracy. Based on the aforementioned improvements, this study proposes the Multi-branch head rotation-equivariant single-stage Detector (MessDet), which achieves state-of-the-art performance on the challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and DIOR-R with an exceptionally low parameter count.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Detection on MODA](<https://www.sota2.com/research/sota/object-detection-on-moda.md>): Object Detection; MODA; AP (Car): 90.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.09896>)

[PDF](<https://arxiv.org/pdf/2507.09896.pdf>)