H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised
Oriented Object Detection
- URL: http://arxiv.org/abs/2304.04403v4
- Date: Mon, 16 Oct 2023 15:12:19 GMT
- Title: H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised
Oriented Object Detection
- Authors: Yi Yu, Xue Yang, Qingyun Li, Yue Zhou, Gefan Zhang, Feipeng Da, Junchi
Yan
- Abstract summary: We present H2RBox-v2, to bridge the gap between HBox-supervised and RBox-supervised oriented object detection.
To our best knowledge, H2RBox-v2 is the first symmetry-aware self-supervised paradigm for oriented object detection.
- Score: 55.3948651109885
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the rapidly increasing demand for oriented object detection, e.g. in
autonomous driving and remote sensing, the recently proposed paradigm involving
weakly-supervised detector H2RBox for learning rotated box (RBox) from the more
readily-available horizontal box (HBox) has shown promise. This paper presents
H2RBox-v2, to further bridge the gap between HBox-supervised and
RBox-supervised oriented object detection. Specifically, we propose to leverage
the reflection symmetry via flip and rotate consistencies, using a
weakly-supervised network branch similar to H2RBox, together with a novel
self-supervised branch that learns orientations from the symmetry inherent in
visual objects. The detector is further stabilized and enhanced by practical
techniques to cope with peripheral issues e.g. angular periodicity. To our best
knowledge, H2RBox-v2 is the first symmetry-aware self-supervised paradigm for
oriented object detection. In particular, our method shows less susceptibility
to low-quality annotation and insufficient training data compared to H2RBox.
Specifically, H2RBox-v2 achieves very close performance to a rotation
annotation trained counterpart -- Rotated FCOS: 1) DOTA-v1.0/1.5/2.0:
72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%; 2) HRSC: 89.66% vs. 88.99%; 3)
FAIR1M: 42.27% vs. 41.25%.
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