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 |
|
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 |
- |
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 |
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 |
✅ |
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 |
|
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 |
✅ |
BBAVectors |
ResNet101 |
72.32 |
WACV2021 |
Pytorch |
keypoint based |
✅ |
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 |
CVPR(oral) |
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 |
arXiv:2101.06849 |
Pytorch |
critical feature, label assign, refine |
✅ |
R3Det |
ResNet101 |
73.79 |
AAAI2021 |
TF, Pytorch |
refined single stage, feature alignment |
✅ |
SDCDet |
ResNet101 |
73.89 |
PRAI2021 |
- |
instance segmentation direction correction |
|
RSDet/RSDet++ |
ResNet152 |
74.10 |
AAAI2021/arXiv:2109.11906 |
TF |
quadrilateral bbox, angular boundary problem |
✅ |
SegmRDet |
ResNet50 |
74.14 |
Neurocomputing |
- |
segmentation-baed, new training and inference |
|
CenterRot |
ResNet152 |
74.75 |
Remote Sensing |
- |
anchor free, deformable-fpn, csl |
|
MEAD |
ResNet101 |
74.80 |
Applied Intelligence |
- |
mechanism anchor free, mask guided, refine feature |
|
Gliding Vertex |
ResNet101 |
75.02 |
TPAMI arXiv:1911.09358 |
Pytorch |
quadrilateral bbox |
✅ |
OSSDet |
ResNeXt-10 |
75.08 |
JSTARS |
- |
feature enhancement and alignment |
|
EFN |
U-Net |
75.27 |
Preprints |
- |
Field-based |
✅ |
SAR |
ResNet152 |
75.26 |
Access |
- |
boundary problem |
✅ |
TricubeNet |
Hourglass104 |
75.26 |
arXiv:2104.11435 |
code |
2D tricube kernel |
✅ |
Mask OBB |
ResNeXt101 |
75.33 |
Remote Sensing |
- |
attention, multi-task |
✅ |
SAOA |
ResNet101 |
75.41 |
ICIG2021 |
- |
anchor free, spatial self-attention |
|
Zand et al. |
DarkNet53 |
75.5 |
TGRS |
- |
angle classification |
|
TS4Net |
ResNet101 |
75.63 |
arXiv:2108.03116 |
- |
label assign |
|
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 |
|
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 |
|
CSL |
ResNet152 |
76.17 |
ECCV2020 |
TF: CSL_RetinaNet, 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 |
|
OWSR |
Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) |
76.36 |
CVPR2019 WorkShop |
- |
enhanced FPN |
|
SLA |
ResNet50 |
76.36 |
Remote Sensing |
Pytorch |
sparse label assignment |
✅ |
OPLD |
ResNet101 |
76.43 |
J-STARS |
Pytorch |
boundary problem, point-guided |
✅ |
Polar Ray |
ResNet101 |
76.50 |
ACM MM2021 |
- |
polar rays representation |
✅ |
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 |
point-based, reppoints |
✅ |
SCRDet++ |
ResNet101 |
76.81 |
arXiv:2004.13316 |
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 |
arXiv:2109.12848 |
Pytorch |
gaussian heatmap labeling |
✅ |
DCL |
ResNet152 |
77.37 |
CVPR2021 |
TF |
boundary problem |
✅ |
MSFF |
ResNet50 |
77.46 |
JSTARS |
- |
rotation invariance features |
|
RIDet |
ResNet50 |
77.62 |
GRSL |
Pytorch, TF |
quad., representation ambiguity |
✅ |
RDD |
ResNet101 |
77.75 |
Remote Sensing |
Pytorch |
rotation-decoupled |
|
OSKDet |
ResNet101 |
77.81 |
arXiv:2104.08697 |
- |
keypoint localization (very similar to FR-Est) |
|
CG-Net |
ResNet101 |
77.89 |
arXiv:2103.11399 |
Pytorch |
attention |
|
HSP |
ResNet101 |
78.01 |
TGRS |
hierarchical semantic propagatio |
|
|
Oriented RepPoints |
ResNet101 |
78.12 |
arXiv:2105.11111 |
Pytorch |
point-based, reppoints |
✅ |
FoRDet |
VGG16 |
78.13 |
TGRS |
- |
refinenet |
|
AProNet |
ResNet101 |
78.16 |
ISPRS |
Pyrotch |
axis projection-based angle learning, feature enhancement |
|
FR-Est |
ResNet101-DCN |
78.49 |
TGRS |
- |
point-based estimator |
✅ |
DARDet |
ResNet50 |
78.74 |
GRSL |
Pytorch |
varifocalnet, dcn, piou |
|
FCOSR |
ResNeXt-101 |
78.90 |
arXiv:2111.10780 |
Pytorch |
anchor free |
|
S2A-Net |
ResNet50/ResNet101 |
79.42/79.15 |
TGRS |
Pytorch |
refined single stage, feature alignment |
✅ |
OFA-Net |
ResNet101 |
79.52 |
PRICAI2021 |
- |
refined single stage, feature alignment |
|
O2DETR |
ResNet50 |
79.66 |
arXiv:2106.03146 |
- |
deformable detr, transformer |
✅ |
ROSD |
ResNet101 |
79.76 |
Access |
- |
refined single stage, feature alignment |
|
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 |
|
ADT-Det |
ResNet152 |
79.95 |
Remote Sensing |
- |
feature pyramid transformer, feature refineent |
|
ReDet |
ReR50-ReFPN |
80.10 |
CVPR2021 |
Pytorch |
rotation-equivariant, rotation-invariant roI align |
✅ |
GWD |
ResNet152 |
80.23 |
ICML2021 |
TF, Pytorch code (YOLOv5-GWD) |
boundary discontinuity, square-like problem, gaussian wasserstein distance loss |
✅ |
O2MER |
ResNet50 |
80.47 |
arXiv:2112.00504 |
- |
consistent geometric constraint |
|
KLD |
ResNet152 |
80.63 |
NeurIPS2021 |
TF, Pytorch code (YOLOv5-KLD) |
Kullback-Leibler divergence, high-precision, scale invariance |
✅ |
AOPG |
ResNet50/ResNet101 |
80.66/80.19 |
arXiv:2110.01931 |
Pytorch |
anchor free, feature align |
|
Oriented R-CNN |
ResNet50/ResNet101 |
80.87/80.52 |
ICCV2021 |
Pytorch |
Rotation FPN, Gliding Vertex |
|