https://orcid.org/0000-0003-1290-0738
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ABSTRACT
Horizontal bounding boxes are inflexible for precisely locating geospatial objects with arbitrary orientations in high-resolution remote sensing images. Recently, rotation detectors with oriented bounding boxes have been found to have a positive effect on the detection of arbitrary-oriented objects. However, this method usually suffers from the requirement of a heavy network structure to learn orientation information. Therefore, a structurally re-parameterized rotation detector (SRep-RDet) for arbitrary-oriented objects in high-resolution remote sensing images is proposed. (1) A structurally re-parameterized backbone network, RepVGG-B1g2, is introduced to the detector based on RetinaNet to decouple the training-time multi-branch and inference-time single-path architecture. (2) The multiscale features of RepVGG-B1g2 are refined by using a lightweight channel attention structure. (3) The multiscale features are fused by structurally re-parameterizing a portion of the convolution layers in the feature pyramid network (FPN). (4) Arbitrary-oriented objects are detected by using a rotation detector with oriented bounding boxes. The experimental results on DOTA and HRSC2016 datasets achieve competitive performance.
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
Data openly available in a public repository that issues datasets with DOIs
The data that support the findings of this study are openly available in DOTA at http://doi.org/10.1109/CVPR.2018.00418 and HRSC2016 at http://doi.org/10.5220/00061206032 40331, reference number (Xia et al. Citation2018) and (Liu et al. Citation2017).