| FR-O (DOTA) |
ResNet101 |
52.93 |
CVPR2018 |
MXNet |
DOTA dataset, baseline |
✅ |
| IENet |
ResNet101 |
57.14 |
arXiv:1912.00969 |
- |
anchor free |
|
| TOSO |
ResNet101 |
57.52 |
ICASSP2020 |
- |
geometric transformation |
|
| Wang et al. |
HRNet |
58.29 |
Comput. Electr. Eng. |
- |
anchor free |
|
| PIoU Loss |
DLA-34 |
60.5 |
ECCV2020 |
PyTorch |
IoU loss, anchor free |
✅ |
| R2CNN |
ResNet101 |
60.67 |
arXiv:1706.09579 |
TF |
scene text, multi-task, different pooled sizes, baseline |
✅ |
| RRPN |
ResNet101 |
61.01 |
TMM arXiv:1703.01086 |
TF |
scene text, rotation proposals, baseline |
✅ |
| Axis Learning |
ResNet101 |
65.98 |
Remote Sensing |
Pytorch |
single stage, anchor free |
✅ |
| Li et al. |
ResNet50 |
66.01 |
IGARSS2021 |
- |
refine, feature alignment |
|
| MARNet |
ResNet101 |
67.19 |
IJRS |
- |
based on scrdet |
|
| ICN |
ResNet101 |
68.16 |
ACCV2018 |
- |
image cascade, multi-scale |
✅ |
| GSDet |
ResNet101 |
68.28 |
TIP |
- |
scale reasoning |
|
| RADet |
ResNeXt101 |
69.09 |
Remote Sensing |
- |
enhanced FPN, mask rcnn |
|
| KARNET |
ResNet50 |
68.87 |
CISNRC 2020 |
- |
attention denoising, anchor refining |
|
| RoI Transformer |
ResNet101 |
69.56 |
CVPR2019 |
MXNet, PyTorch, MMRotate |
roi transformer |
✅ |
| CAD-Net |
ResNet101 |
69.90 |
TGRS arXiv:1903.00857 |
- |
attention |
|
| ProbIoU |
ResNet50 |
70.04 |
arXiv:2106.06072 |
TF |
gaussian bounding boxes, hellinger distance |
✅ |
| ROTP |
ResNet101 |
70.29 |
arXiv:2202.06565 |
- |
solar corona heatmap, key points, head of object |
|
| A2S-Det |
ResNet101 |
70.64 |
Remote Sensing |
- |
label assign |
|
| AOOD |
ResNet101 |
71.18 |
Neural Computing and Applications |
- |
attention + R-DFPN |
|
| CGP Box |
ResNet18 |
71.35 |
IJRS |
- |
center-guide points |
|
| Zhou et al. |
CSPDarknet53 |
71.5 |
ijgi |
- |
enhanced fpn |
|
| ACE |
DLA34 |
71.7 |
TIP |
- |
corner point |
✅ |
| Pei et al. |
ResNet101 |
71.76 |
IGRASS2021 |
- |
enhanced FPN |
|
| Cascade-FF |
ResNet152 |
71.80 |
ICME2020 |
- |
refined retinanet + feature fusion |
|
| SCPNet |
Hourglass104 |
72.20 |
GRSL |
- |
corner points |
|
| P-RSDet |
ResNet101 |
72.30 |
Access |
- |
anchor free, polar coordinates |
✅ |
| Zhang et al. |
ResNet101 |
72.37 |
GSIS |
- |
refine-stage |
|
| ROPDet |
ResNet101-DCN |
72.42 |
J REAL-TIME IMAGE PR |
- |
point set representation |
|
| SCRDet |
ResNet101 |
72.61 |
ICCV2019 |
TF: R2CNN++, IoU-Smooth L1: RetinaNet-based, R3Det-based |
attention, angular boundary problem |
✅ |
| O2-DNet |
Hourglass104 |
72.8 |
ISPRS, arXiv:1912.10694 |
- |
centernet, anchor free |
✅ |
| HRPNet |
HRNet-W48 |
72.83 |
GRSL |
- |
polar |
|
| SARD |
ResNet101 |
72.95 |
Access |
- |
IoU-based weighted loss |
|
| GLS-Net |
ResNet101 |
72.96 |
Remote Sensing |
- |
attention, saliency pyramid |
|
| ProjBB |
ResNet101 |
73.03 |
Access |
code, codebase |
new definition of bounding box |
|
| DRN |
Hourglass104 |
73.23 |
CVPR2020 |
code |
centernet, feature selection module, dynamic refinement head, new dataset (SKU110K-R) |
✅ |
| FADet |
ResNet101 |
73.28 |
ICIP2019 |
- |
attention |
|
| RBA-CenterNet |
ResNet101 |
73.41 |
IJCNN |
- |
centernet, refine feature |
|
| MFIAR-Net |
ResNet152 |
73.49 |
Sensors |
- |
feature attention, enhanced FPN |
|
| CFC-NET |
ResNet101 |
73.50 |
TGRS |
PyTorch |
critical feature, label assign, refine |
✅ |
| Dual-Det |
ResNet18 |
73.62 |
IJRS |
code |
keypoint-based |
|
| Li et al. |
CSP-Hourglass |
73.70 |
GRSL |
- |
CSP-Hourglass Net |
|
| R3Det |
ResNet101 |
73.79 |
AAAI2021 |
TF, r3det-on-mmdetection, r3det-pytorch, MMRotate |
refined single stage, feature alignment |
✅ |
| SDCDet |
ResNet101 |
73.89 |
PRAI2021 |
- |
instance segmentation direction correction |
|
| Geng et al. |
ResNet101 |
73.92 |
GRSL |
- |
anchor free, angle encoding |
|
| Free3Net |
ResNet101 |
74.04 |
TMM |
- |
anchor free, gliding vertex |
✅ |
| SegmRDet |
ResNet50 |
74.14 |
Neurocomputing |
- |
segmentation-baed, new training and inference |
|
| Hou et al. |
ResNet101 |
74.44 |
TIP |
- |
enhanced FPN, feature alignment |
|
| Wu et al. |
ResNet50 |
74.45 |
J Electron Imaging |
- |
enhanced FPN, feature alignment |
|
| CenterRot |
ResNet152 |
74.75 |
Remote Sensing |
- |
anchor free, deformable-fpn, csl |
|
| MEAD |
ResNet101 |
74.80 |
Applied Intelligence |
- |
mechanism anchor free, mask guided, refine feature |
|
| TS4Net |
ResNet101 |
74.82 |
Neurocomputing |
- |
label assign |
|
| FEDet |
ResNet50 |
74.89 |
ICCSE 2021 |
- |
refine feature, angle constraint |
|
| SRep-RDet |
RepVGG-B1g2 |
74.89 |
IJRS |
- |
refine feature, attention, repvgg |
|
| Yuan et al. |
HRNet32 |
74.97 |
ICCTIT2021 |
- |
centernet-r |
|
| Gliding Vertex |
ResNet101 |
75.02 |
TPAMI arXiv:1911.09358 |
PyTorch, MMRotate |
quadrilateral bbox |
✅ |
| OSSDet |
ResNeXt101 |
75.08 |
JSTARS |
- |
feature enhancement and alignment |
|
| LO-Det |
Darknet53 |
75.24 |
TGRS, arXiv:2209.07709 |
PyTorch |
lightweight |
✅ |
| EFN |
U-Net |
75.27 |
Preprints |
- |
Field-based |
✅ |
| SAR |
ResNet152 |
75.26 |
Access |
- |
boundary problem |
✅ |
| TricubeNet |
Hourglass104 |
75.26 |
WACV2022 |
code |
2D tricube kernel |
✅ |
| Mask OBB |
ResNeXt101 |
75.33 |
Remote Sensing |
- |
attention, multi-task |
✅ |
| BBAVectors |
ResNet101 |
75.36 |
WACV2021 |
PyTorch |
keypoint based |
✅ |
| SAOA |
ResNet101 |
75.41 |
ICIG2021 |
- |
anchor free, spatial self-attention |
|
| Yuan et al. |
ResNet101 |
75.43 |
TGRS |
- |
feature alignment, continuous boundary |
|
| Zand et al. |
DarkNet53 |
75.5 |
TGRS |
- |
angle classification |
|
| MSFF |
- |
75.60 |
ICCECE 2022 |
- |
multi-scale feature fusion |
|
| RODFormer |
ViT-B4 |
75.60 |
Sensors |
- |
vision transformer |
|
| FFA |
ResNet101 |
75.7 |
ISPRS |
- |
enhanced FPN, rotation proposals |
|
| CBDA-Net |
DLA-34-DCN |
75.74 |
TGRS |
- |
dual attention |
|
| APE |
ResNeXt101(32x4) |
75.75 |
TGRS arXiv:1906.09447 |
- |
adaptive period embedding, length independent IoU (LIIoU) |
✅ |
| R4Det |
ResNet152 |
75.54 |
Image Vis Comput |
- |
feature recursion and refinement |
|
| SurroundNet |
ResNet152 |
75.88 |
Remote Sensing |
- |
attention, anchor free, quad |
|
| RIE |
HRGANet-W48 |
75.94 |
Remote Sensing |
- |
center-based rotated inscribed ellipse |
|
| F3-Net |
ResNet152 |
76.02 |
Remote Sensing |
- |
feature fusion and filtration |
|
| CenterMap OBB |
ResNet101 |
76.03 |
TGRS |
- |
center-probability-map |
|
| DA-Net |
ResNet101 |
76.11 |
GRSL |
- |
feature alignment |
|
| MDL-p |
ResNet101 |
76.16 |
arXiv:2204.00840 |
- |
gaussian modeling |
|
| CSL |
ResNet152 |
76.17 |
ECCV2020 |
TF, MMRotate, Pytorch: YOLOv5_DOTA_OBB (CSL) |
angular boundary problem |
✅ |
| MRDet |
ResNet101 |
76.24 |
TGRS |
- |
arbitrary-oriented rpn, multiple subtasks |
|
| AFC-Net |
ResNet101 |
76.27 |
Neurocomputing |
- |
adaptive feature concatenate |
|
| RSDet/RSDet++ |
ResNet152 |
76.30 |
AAAI2021/TCSVT |
TF |
quadrilateral bbox, angular boundary problem |
✅ |
| OWSR |
Ensemble |
76.36 |
CVPR2019 WorkShop |
- |
enhanced FPN |
|
| SLA |
ResNet50 |
76.36 |
Remote Sensing |
PyTorch |
sparse label assignment |
✅ |
| SE2-Det |
ResNet101 |
76.42 |
Remote Sensing |
- |
enhanced fpn |
|
| OPLD |
ResNet101 |
76.43 |
J-STARS |
PyTorch |
boundary problem, point-guided |
✅ |
| Polar Ray |
ResNet101 |
76.50 |
ACM MM2021 |
- |
polar rays representation |
✅ |
| SIoU |
ResNet50 |
76.54 |
JSTARS |
- |
splicing intersection over union |
✅ |
| R3Det++ |
ResNet152 |
76.56 |
arXiv:2004.13316 |
TF |
refined single stage, feature alignment, denoising |
✅ |
| PolarDet |
ResNet101 |
76.64 |
IJRS arXiv:2010.08720 |
- |
polar, center-semantic |
✅ |
| Beyond Bounding-Box |
ResNet152 |
76.67 |
CVPR2021 |
PyTorch, MMRotate |
point-based, reppoints |
✅ |
| OAN |
ResNeX50 |
76.73 |
arXiv:2212.13136 |
PyTorch |
objectness activation network, efficient |
✅ |
| SCRDet++ |
ResNet101 |
76.81 |
TPAMI |
TF |
angular boundary problem, denoising |
✅ |
| DAFNe |
ResNet101 |
76.95 |
arXiv:2109.06148 |
PyTorch |
single stage, anchor free, center-to-corner regression |
|
| DAL+S2A-Net |
ResNet50 |
76.95 |
AAAI2021 |
PyTorch |
label assign |
✅ |
| GGHL |
DarkNet53 |
76.95 |
TIP |
PyTorch |
gaussian heatmap labeling |
✅ |
| EAutoDet |
DarkNet53 |
77.05 |
arXiv:2203.10747 |
- |
nas, yolov5 |
✅ |
| Yu et al. |
Res2Net50 |
77.18 |
JSTARS |
- |
boundary-aware vectors, centernet |
|
| CoF-Net |
ResNet50 |
77.2 |
TGRS |
- |
coarse-to-fine, geometric constraints, spatial-spectral nonocal features |
|
| DCL |
ResNet152 |
77.37 |
CVPR2021 |
TF |
boundary problem |
✅ |
| CSL+DCL |
ResNet152 |
77.37 |
IJCV |
TF |
boundary problem |
✅ |
| CLT-Det |
ResNet101 |
77.45 |
TGRS |
- |
transformer, correlation learning |
✅ |
| MSFF |
ResNet50 |
77.46 |
JSTARS |
- |
rotation invariance features |
|
| RIDet |
ResNet50 |
77.62 |
GRSL |
PyTorch, TF |
quad., representation ambiguity |
✅ |
| Oriented RepPoints |
Swin-Tiny |
77.63 |
CVPR2022 |
PyTorch, MMRotate |
point-based, reppoints |
✅ |
| RSP |
ViTAEv2-S |
77.72 |
arXiv:2204.02825 |
PyTorch |
remote sensing pretrain |
✅ |
| RDD |
ResNet101 |
77.75 |
Remote Sensing |
PyTorch |
rotation-decoupled |
|
| CenterOBB |
DLA-34 |
77.85 |
Remote Sensing |
- |
angle classification, centernet |
✅ |
| FSDet |
ResNet50 |
77.85 |
TGRS |
- |
label assign, feature refinemen, anchor free |
✅ |
| CG-Net |
ResNet101 |
77.89 |
arXiv:2103.11399 |
PyTorch |
attention |
|
| HSP |
ResNet101 |
78.01 |
TGRS |
- |
hierarchical semantic propagation |
|
| FoRDet |
VGG16 |
78.13 |
TGRS |
- |
refinenet |
|
| AProNet |
ResNet101 |
78.16 |
ISPRS |
Pyrotch |
axis projection-based angle learning, feature enhancement |
|
| MGAR |
DarkNet53 |
78.29 |
arXiv:2209.02884 |
- |
angle classification |
✅ |
| TransConvNet |
Swin Transformer |
78.41 |
Remote Sensing |
- |
enhanced fpn, self attention, transformer |
|
| FR-Est |
ResNet101-DCN |
78.49 |
TGRS |
- |
point-based estimator |
✅ |
| TIOE-Det |
- |
78.69 |
ISPRS |
PyTorch |
misaligned tasks, orientation estimation |
✅ |
| DARDet |
ResNet50 |
78.74 |
GRSL |
PyTorch |
varifocalnet, dcn, piou |
|
| TS-Conv |
DarkNet53 |
78.75 |
arXiv:2209.02200 |
PyTorch |
dynamic label assignment, task-wise samping |
✅ |
| DHRec |
ResNet152 |
78.83 |
TPAMI |
PyTorch |
double horizontal rectangles |
|
| FCOSR |
ResNeXt101 |
78.90 |
arXiv:2111.10780 |
PyTorch |
anchor free |
|
| P2P |
ResNet101 |
79.15 |
AAAI2022 |
- |
poly iou loss |
✅ |
| SASM |
ResNeXt101 |
79.17 |
AAAI2022 |
MMRotate, PyTorch |
label assign |
✅ |
| AO2-DETR |
ResNet50 |
79.22 |
arXiv:2205.12785 |
PyTorch |
detr, feature refinement |
✅ |
| GSNet |
ResNet101 |
79.37 |
arXiv:2204.02033 |
- |
enhanced fpn |
|
| S2A-Net |
ResNet50/ResNet101 |
79.42/79.15 |
TGRS |
PyTorch, MMRotate |
refined single stage, feature alignment |
✅ |
| OFA-Net |
ResNet101 |
79.52 |
PRICAI2021 |
- |
refined single stage, feature alignment |
|
| TARDet |
ResNet101 |
79.57 |
CVPRW2022 |
- |
anchor free, feature alignment |
|
| RBox |
ResNet50 |
79.59 |
CVPR2022 |
- |
transformer, feature sampling and grouping |
✅ |
| O2DETR |
ResNet50 |
79.66 |
arXiv:2106.03146 |
- |
deformable detr, transformer |
✅ |
| ROSD |
ResNet101 |
79.76 |
Access |
- |
refined single stage, feature alignment |
|
| RAOD |
ResNet101 |
79.78 |
Applied Intelligence |
- |
enhanced fpn, feature refine |
|
| SES-Net |
ResNet50 |
79.80 |
arXiv:2111.03420 |
- |
sampling equivariance, self-attention |
✅ |
| SARA |
ResNet50/ResNet101 |
79.91/79.13 |
Remote Sensing |
- |
self-adaptive aspect ratio anchor, refine |
|
| ARP+R-EIoU |
YOLOv5x6 |
79.93 |
arXiv:2109.10187 |
- |
area ratio of parallelogram, R-EIoU, yolov5 |
|
| GF-CSL |
ResNet101 |
79.94 |
TGRS |
PyTorch |
circular smooth label, gaussian focal loss |
✅ |
| ADT-Det |
ResNet152 |
79.95 |
Remote Sensing |
- |
feature pyramid transformer, feature refinement |
|
| ReDet |
ReR50-ReFPN |
80.10 |
CVPR2021 |
PyTorch, MMRotate |
rotation-equivariant, rotation-invariant roI align |
✅ |
| G-Rep |
Swin-Tiny |
80.12 |
arXiv:2205.11796 |
MMRotate |
pointset-based, gaussian modeling |
✅ |
| PCI |
ReR50-ReFPN |
80.15 |
TIP |
- |
progressive context-dependent inference |
✅ |
| GWD |
ResNet152 |
80.23 |
ICML2021 |
TF, MMRotate |
boundary discontinuity, square-like problem, gaussian wasserstein distance loss |
✅ |
| DEA |
ReR50-ReFPN |
80.37 |
TGRS |
PyTorch |
dynamic enhancement anchor |
✅ |
| FDOL |
ReR50-ReFPN |
80.41 |
TGRS |
- |
frequency analysis, self-attention |
✅ |
| O2MER |
ResNet50 |
80.43 |
arXiv:2112.00504 |
- |
consistent geometric constraint |
|
| DODet |
ResNet50 |
80.62 |
TGRS |
PyTorch |
oriented proposal network, localization-guided head |
|
| KLD |
ResNet152 |
80.63 |
NeurIPS2021 |
TF, MMRotate |
Kullback-Leibler divergence, high-precision, scale invariance |
✅ |
| AOPG |
ResNet50/ResNet101 |
80.66/80.19 |
arXiv:2110.01931 |
PyTorch |
anchor free, feature align |
|
| Li et al. |
ResNet101 |
80.68 |
Remote Sensing |
- |
enhance fpn, polar |
|
| CGCDet |
ResNet50 |
80.70 |
TNNLS |
PyTorch |
geometric consistent constraint, label assignment |
✅ |
| AFF-Det |
ResNet50 |
80.73 |
Acm T Multim Comput. |
- |
enhanced fpn |
|
| PP-YOLOE-R-x |
CRN-x |
80.73 |
arXiv:2211.02386 |
Paddle |
real-time, yolo |
✅ |
| Oriented R-CNN |
ResNet50/ResNet101 |
80.87/80.52 |
ICCV2021 |
PyTorch, MMRotate |
Rotation FPN, Gliding Vertex |
|
| OSKDet |
ResNet101 |
80.91 |
CVPR2022 |
- |
keypoint localization |
✅ |
| KFIoU |
Swin-Tiny |
80.93 |
ICLR2023 |
TF, MMRotate |
Gaussian modeling, kalman filter |
✅ |
| QPDet |
ResNet50 |
81.00 |
TGRS |
PyTorch |
quadrant point regression, rotated box refinement |
✅ |
| Point RCNN |
ReR50-ReFPN/Swin-Tiny |
80.71/81.32 |
Remote Sensing |
- |
point based, cascade rcnn |
✅ |
| SSEDet |
ResNet50 |
81.08 |
GRSL |
- |
circle theorem, short-side excursion |
✅ |
| RTMDet |
- |
81.33 |
arXiv:2212.07784 |
MMRotate |
real-time |
✅ |