A Unified Remote Sensing Object Detector Based on Fourier Contour Parametric Learning

A unified object detector needs to integrate various abilities for adapting to different remote sensing object detection tasks. However, there is a lack of a feasible way to integrate multigrained object detection requirements i.e., horizontal bounding box (HBB), oriented bounding box (OBB), and instance segmentation (InSeg) into a unified detection way. Then, it often has to design specific parametric learning ways and their corresponding architectures, which cannot be finely adaptive to various kinds of object detection tasks. Therefore, in this article, a new benchmark is set up to integrate multigrained object detection requirements of HBB, OBB, and InSeg into one challenging task of arbitrary-shaped object contour detection. At the same time, a unified object contour detector (UniconDet) is proposed for achieving multigrained object detection from complicated remote sensing scenes. First, a Fourier contour parametric modeling (FCPM) is defined to project arbitrary-shaped object contours from the spatial domain into the frequency domain. Then, it can unify spatial parametric representations of HBB, OBB, and InSeg as frequency coefficient representations, which can be used for realizing a more generic and robust parametric regression. Second, a multiview cross-attention (MVCA) feature extraction way is designed at each scale of the regression layer, which can assist UniconDet in perceiving Fourier contour parameters by exploring the coupled relations between different discrete contour sampling periods of each object. Third, a center-contour enhancing regression layer (C2-ERL) is designed to generate regional guidance and cascade contour propagation, which can ensure a more accurate center point prediction and Fourier contour parameter regression. Finally, extensive experiments are carried out on benchmarks of HBB, OBB, InSeg, and new multigrained object detection, and the results indicate that our proposed UniconDet can obtain superior performance. The source code is available at https://github.com/ZhAnGToNG1/UniconDet.