Structurally re-parameterized rotation detector for arbitrary-oriented objects in high-resolution remote sensing images

Abstract

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.