Project Page

Introduction

Oriented object detection in aerial images poses a significant challenge due to their varying sizes and orientations. Current state-of-the-art detectors typically rely on either two-stage or one-stage approaches, often employing Anchor-based strategies, which can result in computationally expensive operations due to the redundant number of generated anchors during training. In contrast, Anchor-free mechanisms offer faster processing but suffer from a reduction in the number of training samples, potentially impacting detection accuracy. To address these limitations, we propose the Hybrid-Anchor Rotation Detector (HA-RDet), which combines the advantages of both anchor-based and anchor-free schemes for oriented object detection. By utilizing only one preset anchor for each location on the feature maps and refining these anchors with our Orientation-Aware Convolution technique, HA-RDet achieves competitive accuracies, including 75.41 mAP on DOTA-v1, 65.3 mAP on DIOR-R, and 90.2 mAP on HRSC2016, against current anchor-based state-of-the-art methods, while significantly reducing computational resources.

Installation

Data preparation and download * DOTA-v1.0: download * HRSC2016: download * DIOR-R: download

HA-RDet mmrotate tools configs data split_ss_dota trainval annfiles images test annfiles images DIOR-R trainval test HRSC ImageSets FullDataSets

Our experiment relies on the MMRotate framework provided by Open MMLab. MMRotate depends on PyTorch, MMCV and MMDetection. Quick steps for installation follows as:

• Git clone

git clone https://github.com/PhucNDA/HA-RDet

• Environment setup

conda create -n [NAME] python=3.7 pytorch==1.7.0 cudatoolkit=10.1 torchvision -c pytorch -y conda activate [NAME] pip install openmim mim install mmcv-full mim install mmdet cd 'Hybrid-Anchor-Rotation-Detector' pip install -r requirements/build.txt pip install -v -e .

Training and Inference

• Training command:

``` python tools/train.py ${CONFIG_FILE} [optional arguments]

Example:

python tools/train.py configs/hardet/hardetbaseliner50fpn1xdota_le90.py ```

• Inference command for online submission: python ./tools/test.py \ configs/ha_rdet/hardet_baseline_r50_fpn_1x_dota_le90.py \ checkpoints/SOME_CHECKPOINT.pth --format-only \ --eval-options submission_dir=[SAVE_FOLDER]

• Visualize the results python ./tools/test.py \ configs/ha_rdet/hardet_baseline_r50_fpn_1x_dota_le90.py \ checkpoints/SOME_CHECKPOINT.pth --show-dir [SAVE_FOLDER]

Benchmark and Model Zoo

DOTA-v1.0 dataset

| Model | Backbone | #anchors | VRAM (GB) | #params | FPS | mAP | Config | Download | | ------ |:-------------:|:----------------------:|:-----------------------------------------------------:|:-------------------------:|:----:|:----:|:---:|:--:| | S2A-Net| ResNet50+FPN | 1 | 4.6 | ~39M | 15.5 | 74.19 | - | - | | Oriented R-CNN| ResNet50+FPN | 20 | 14.2 | ~41M | 13.5 | 75.69 | - | - | | HA-RDet (ours) | ResNet50+FPN | 1 | 6.8 | ~56M | 12.1 | 75.41 | config | model / log | | HA-RDet (ours) | ResNet101+FPN | 1 | - | - | - | 76.02 | config | model / log | | HA-RDet (ours) | ResNeXt101DCNv2+FPN | 1 | - | - | - | 77.012 | <a href="https://github.com/PhucNDA/HA-RDet/blob/main/configs/hardet/hardetbaselinerx101dcnfpn1xdotale90.py">config | <a href="https://drive.google.com/file/d/129jCteJpW-13MxClbZP7eHuRY9HJPTH/view?usp=drivelink">model / <a href="https://github.com/PhucNDA/HA-RDet/blob/main/logs/hardetbaselinerx101dcnfpn1xdotale90.txt">log |

HRSC2016

| Model | Backbone | #anchors | mAP (VOC 07) | mAP (VOC 12) | |:-----:|:--------:|:-------:|:-------:|:-------:| | S2A-Net | ResNet101+FPN | 1 | 90.17 | 95.01 | | AOPG | ResNet101+FPN | 1 | 90.34 | 96.22 | | HA-RDet (ours) | ResNeXt101_DCNv2+FPN | 1 | 90.2 | 95.32 |

DIOR-R

| Model | Backbone | mAP | |:-----:|:--------:|:---:| | HA-RDet | ResNeXt101_DCNv2+FPN | 65.3 |

Visualization

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