EllipticNet： Equation-based Remote Sensing Oriented Object Detection Network

TU Kelong; QING Yaxian; LI Zhenqiang; Yang Chao; QI Kunlun; WU Huayi

doi:10.11834/jrs.20243280

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EllipticNet： Equation-based Remote Sensing Oriented Object Detection Network
Pages: 1-19(2024)
Published Online： 11 March 2024 ，
DOI： 10.11834/jrs.20243280

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涂可龙，卿雅娴，李真强，杨超，祁昆仑，吴华意.XXXX.EllipticNet：基于椭圆方程的遥感有向目标检测.遥感学报，XX（XX）： 1-19

TU Kelong，QING Yaxian，LI Zhenqiang，Yang Chao，QI Kunlun，WU Huayi. XXXX. EllipticNet： Equation-based Remote Sensing Oriented Object Detection Network. National Remote Sensing Bulletin， XX（XX）：1-19
涂可龙，卿雅娴，李真强，杨超，祁昆仑，吴华意.XXXX.EllipticNet：基于椭圆方程的遥感有向目标检测.遥感学报，XX（XX）： 1-19 DOI： 10.11834/jrs.20243280.

TU Kelong，QING Yaxian，LI Zhenqiang，Yang Chao，QI Kunlun，WU Huayi. XXXX. EllipticNet： Equation-based Remote Sensing Oriented Object Detection Network. National Remote Sensing Bulletin， XX（XX）：1-19 DOI： 10.11834/jrs.20243280.

摘要

遥感有向目标检测是计算机视觉领域内一项具有挑战性的任务，传统的水平框表示法无法精确定位尺度各异、方向任意且密集排列的遥感目标。目前广泛采用的五参数有向框表示法，由于方向角的周期性和边的交换性问题，增加了模型训练的复杂度。为了解决上述问题，本文提出了一种基于椭圆方程的遥感有向目标检测模型（Elliptical Equation-based Remote Sensing Oriented Object Detection Network，EllipticNet）。首先，EllipticNet将方向角的预测问题解耦为两个子问题：定量角度回归和旋转方向分类，从而克服五参数有向框表示法的边界不连续性问题；结合椭圆的长短轴以及中心点预测，实现遥感有向目标的精确表示。其次，本文设计了一种椭圆约束的损失函数，通过增强椭圆参数之间的内在几何关系，提高EllipticNet训练的鲁棒性。此外，本文还提出了一种逐层空洞空间卷积池化金字塔模块，显著提升EllipticNet对多尺度特征的表征能力。最后，在DOTA、HRSC2016和UCAS_AOD三个常用的公开遥感数据集上的对比实验表明，本文方法在性能和效率方面均具有竞争力，表明本文方法在遥感有向目标检测中具有一定的实用价值。

Abstract

Objective.Remote sensing oriented object detection is a challenging task in the field of computer vision

as traditional horizontal bounding box representations cannot accurately locate remote sensing targets that are of various scales

arbitrary orientations

and densely arranged. The widely used five-parameter oriented bounding box representation increases the complexity of model training due to the periodicity of the orientation angle and the interchangeability of edges. To address these issues

this paper proposes an elliptical equation-based remote sensing oriented object detection network (EllipticNet).

Methods

.EllipticNet decouples the problem of predicting the orientation angle into two sub-problems: quantitative angle regression and rotation direction classification. Moreover

the proposed method combines the major and minor axes of the ellipse and its center to describe the remote sensing oriented target more accurately

thereby overcoming the boundary discontinuity problem of the five-parameter oriented bounding box representation. Additionally

a novel ellipse-constrained loss function is designed to enhance the intrinsic geometric relationship between the ellipse parameters

improving the robustness of EllipticNet training. Furthermore

a layer-wise dilated spatial pyramid pooling module is proposed

significantly enhancing EllipticNet's ability to represent multi-scale features.

Results

.Finally

the proposed method is validated on three commonly used public remote sensing datasets: DOTA

HRSC2016

and UCAS_AOD.

Conclusion

.The results demonstrate that the proposed method is competitive in terms of performance and efficiency

indicating its practical value in remote sensing oriented object detection.

关键词

有向目标检测椭圆方程特征增强高分辨率遥感影像

Keywords

oriented object detectionelliptic equationfeature enhancementhigh resolution remote sensing image

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