Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing

TL;DR

This paper introduces a novel geometric computing mechanism for remote sensing object detection that jointly learns proposals and rotation angles, significantly improving accuracy without extra inference costs.

Contribution

It proposes a simple, effective joint supervision method for oriented object detection using naive geometric computing and an oriented center prior label assignment strategy.

Findings

Outperforms baseline by a large margin

Achieves state-of-the-art results

No extra computational burden during inference

Abstract

Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images. Despite that many methods proposed recently have achieved remarkable performance, most of them directly learn to predict object directions under the supervision of only one (e.g. the rotation angle) or a few (e.g. several coordinates) groundtruth values individually. Oriented object detection would be more accurate and robust if extra constraints, with respect to proposal and rotation information regression, are adopted for joint supervision during training. To this end, we innovatively propose a mechanism that simultaneously learns the regression of horizontal proposals, oriented proposals, and rotation angles of objects in a consistent manner, via naive geometric computing, as one additional steady constraint (see Figure 1). An oriented center prior guided label assignment strategy is proposed for further enhancing the quality of proposals, yielding better performance. Extensive experiments demonstrate the model equipped with our idea significantly outperforms the baseline by a large margin to achieve a new state-of-the-art result without any extra computational burden during inference. Our proposed idea is simple and intuitive that can be readily implemented. Source codes and trained models are involved in supplementary files.