用乔列斯基分解构建高斯分布，解决旋转目标检测的边界不连续问题。
GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection
- 通过乔列斯基分解直接生成高斯分布，避免角度边界断裂
- 在DOTA数据集上性能媲美或超越现有最优模型
- 使用定向椭圆表示物体，缓解圆形目标编码模糊问题
近年来，旋转目标检测（OOD）因适用于遥感中细长物体的检测而受到关注。基于高斯分布的回归损失函数因其形式简洁且可微，逐渐成为主流。然而，现有方法仍依赖于输出旋转边界框（OBB）的回归头，角度边界不连续问题依然存在。本文提出一种新的回归头GauCho，直接基于乔列斯基分解生成高斯分布，理论上可消除边界不连续性，并与当前主流的高斯型损失函数完全兼容。此外，我们主张用定向椭圆（OE）表示旋转目标，其与GauCho之间存在双射关系，能有效缓解圆形物体的编码歧义问题。实验表明，GauCho可作为传统OBB头的可行替代方案，在具有挑战性的DOTA数据集上达到或超过当前最先进检测器的性能。
原文摘要 · Abstract (English)
Oriented Object Detection (OOD) has received increased attention in the past years, being a suitable solution for detecting elongated objects in remote sensing analysis. In particular, using regression loss functions based on Gaussian distributions has become attractive since they yield simple and differentiable terms. However, existing solutions are still based on regression heads that produce Oriented Bounding Boxes (OBBs), and the known problem of angular boundary discontinuity persists. In this work, we propose a regression head for OOD that directly produces Gaussian distributions based on the Cholesky matrix decomposition. The proposed head, named GauCho, theoretically mitigates the boundary discontinuity problem and is fully compatible with recent Gaussian-based regression loss functions. Furthermore, we advocate using Oriented Ellipses (OEs) to represent oriented objects, which relates to GauCho through a bijective function and alleviates the encoding ambiguity problem for circular objects. Our experimental results show that GauCho can be a viable alternative to the traditional OBB head, achieving results comparable to or better than state-of-the-art detectors for the challenging dataset DOTA
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