[AAAI 2026]Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection

Offical implementation of “Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection”

we also add our config in https://github.com/zcablii/LSKNet and https://github.com/YXB-NKU/Strip-R-CNN.

If you encounter any issues while using our code, please check the relevant repositories for related issues first. If none exist, feel free to ask me!

Update[22/2/2025]

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Jittor implementation at github.com/NK-JittorCV/nk-remote

博客

Abstract

While witnessed with rapid development, remote sensing object detection remains challenging for detecting high aspect ratio objects. This paper shows that large strip convolutions are good feature representation learners for remote sensing object detection and can detect objects of various aspect ratios well. Based on large strip convolutions, we build a new network architecture called Strip R-CNN, which is simple, efficient, and powerful. Unlike recent remote sensing object detectors that leverage large-kernel convolutions with square shapes, our Strip R-CNN takes advantage of sequential orthogonal large strip convolutions to capture spatial information. In addition, we enhance the localization capability of remote-sensing object detectors by decoupling the detection heads and equipping the localization head with strip convolutions to better localize the target objects. Extensive experiments on several benchmarks, for example DOTA, FAIR1M, HRSC2016, and DIOR, show that our Strip R-CNN can greatly improve previous work. In particular, our 30M model achieves 82.75% mAP on DOTA-v1.0, setting a new state-of-the-art record.

Introduction

This repository is the official implementation of “Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection” at: arxiv

The master branch is built on MMRotate which works with PyTorch 1.6+.

StripNet backbone code is placed under mmrotate/models/backbones/, and the train/test configure files are placed under configs/strip_rcnn/

Results and models

Imagenet 300-epoch pre-trained Strip R-CNN-T backbone: Download

Imagenet 300-epoch pre-trained Strip R-CNN-S backbone: Download

Please note that the Exponential Moving Average (EMA) strategy was not utilized during the ImageNet pretraining stage. DOTA1.0

Model	mAP	Angle	lr schd	Batch Size	Configs	Download	note
RTMDet-l	81.33	-	3x-ema	8	-	-	Prev. Best
Strip R-CNN-T	81.40	le90	1x	1*8	strip_rcnn_t_fpn_1x_dota_le90	model
Strip R-CNN-S	82.28	le90	1x	1*8	strip_rcnn_s_fpn_1x_dota_le90	model
Strip R-CNN-S*	82.75	le90	1x	1*8	strip_rcnn_s_fpn_1x_dota_le90	model	MoCAE
StripNet-S + Roi_Trans	81.72	le90	1x	1*8	strip_rcnn_s_roitrans_fpn_1x_dota	model

FAIR1M-1.0

Model	mAP	Angle	lr schd	Batch Size	Configs	Download	note

| Strip R-CNN-S | 48.26 | le90 | 1x | 1*8 | strip_rcnn_s_fpn_1x_dota_le90 | model | |

HRSC2016

Model	mAP(07)	mAP(12)	Angle	lr schd	Batch Size	Configs	Download	note
RTMDet-l	90.60	97.10	le90	3x	-	-	-	Prev. Best
ReDet	90.46	97.63	le90	3x	2*4	redet_re50_refpn_3x_hrsc_le90	-	Prev. Best
Strip R-CNN-S	90.60	98.70	le90	3x	1*8	strip_rcnn_s_fpn_3x_hrsc_le90	model

Installation

MMRotate depends on PyTorch, MMCV and MMDetection. Below are quick steps for installation. Please refer to Install Guide for more detailed instruction.

conda create --name openmmlab python=3.8 -y
conda activate openmmlab
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
pip install -U openmim
mim install mmcv-full
mim install mmdet
git clone https://github.com/YXB-NKU/Strip-R-CNN.git
cd Strip-R-CNN
pip install -v -e .

Get Started

Please see get_started.md for the basic usage of MMRotate. We provide colab tutorial, and other tutorials for:

• learn the basics
• learn the config
• customize dataset
• customize model
• useful tools

Acknowledgement

MMRotate is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.

Citation

If you use this toolbox or benchmark in your research, please cite this project.

If you like our work, don’t hesitate to reach out! Let’s work on it and see how far it would go!

@article{yuan2025strip,
  title={Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection},
  author={Yuan, Xinbin and Zheng, ZhaoHui and Li, Yuxuan and Liu, Xialei and Liu, Li and Li, Xiang and Hou, Qibin and Cheng, Ming-Ming},
  journal={arXiv preprint arXiv:2501.03775},
  year={2025}
}

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License

Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first.