H2RBox-v2: Boosting HBox-supervised Oriented Object Detection via Symmetric Learning
With the increasing demand for oriented object detection e.g. in autonomous driving and remote sensing, the oriented annotation has become a labor-intensive work. To make full use of existing horizontally annotated datasets and reduce the annotation cost, a weakly-supervised detector H2RBox for learning the rotated box (RBox) from the horizontal box (HBox) has been proposed and received great attention. This paper presents a new version, H2RBox-v2, to further bridge the gap between HBox-supervised and RBox-supervised oriented object detection. While exploiting axisymmetry via flipping and rotating consistencies is available through our theoretical analysis, H2RBox-v2, using a weakly-supervised branch similar to H2RBox, is embedded with a novel self-supervised branch that learns orientations from the symmetry inherent in the image of objects. Complemented by modules to cope with peripheral issues, e.g. angular periodicity, a stable and effective solution is achieved. To our knowledge, H2RBox-v2 is the first symmetry-supervised paradigm for oriented object detection. Compared to H2RBox, our method is less susceptible to low annotation quality and insufficient training data, which in such cases is expected to give a competitive performance much closer to fully-supervised oriented object detectors. Specifically, the performance comparison between H2RBox-v2 and Rotated FCOS on DOTA-v1.0/1.5/2.0 is 72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%, 89.66% vs. 88.99% on HRSC, and 42.27% vs. 41.25% on FAIR1M.
DOTA1.0
| Backbone | AP50 | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
|---|---|---|---|---|---|---|---|---|
| ResNet50 (1024,1024,200) | 72.59 | 1x | 10.10 | 29.1 | - | 2 | h2rbox_v2-le90_r50_fpn-1x_dota | model | log |
| ResNet50 (1024,1024,200) | 78.25 | 1x | 10.33 | 29.1 | MS+RR | 2 | h2rbox_v2-le90_r50_fpn_ms_rr-1x_dota | model | log |
DOTA1.5
| Backbone | AP50 | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
|---|---|---|---|---|---|---|---|---|
| ResNet50 (1024,1024,200) | 64.76 | 1x | 10.95 | 29.1 | - | 2 | h2rbox_v2-le90_r50_fpn-1x_dotav15 | model | log |
DOTA2.0
| Backbone | AP50 | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
|---|---|---|---|---|---|---|---|---|
| ResNet50 (1024,1024,200) | 50.33 | 1x | 11.02 | 29.1 | - | 2 | h2rbox_v2-le90_r50_fpn-1x_dotav2 | model | log |
HRSC
| Backbone | AP50 | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
|---|---|---|---|---|---|---|---|---|
| ResNet50 (1024,1024,200) | 89.66 | 1x | 5.50 | 45.9 | - | 2 | h2rbox_v2-le90_r50_fpn-6x_hrsc | model | log |
| ResNet50 (1024,1024,200) | 89.56 | 1x | 5.50 | 45.9 | RR | 2 | h2rbox_v2-le90_r50_fpn_rr-6x_hrsc | model | log |
@misc{yu2023h2rboxv2,
title={H2RBox-v2: Boosting HBox-supervised Oriented Object Detection via Symmetric Learning},
author={Yi Yu and Xue Yang and Qingyun Li and Yue Zhou and Gefan Zhang and Feipeng Da and Junchi Yan},
year={2023},
eprint={2304.04403},
archivePrefix={arXiv},
primaryClass={cs.CV}
}