用结构张量表示方向，解决旋转目标检测中的周期性难题。
Structure Tensor Representation for Robust Oriented Object Detection
- 将方向编码为结构张量，融合高斯与角度编码优势
- 五大数据集上表现优于现有方法，角度预测精度高
- 无需额外超参数，可直接嵌入现有检测器
定向目标检测需同时预测位置和方向。由于角度周期性带来的边界不连续与对称性模糊，精确预测方向仍具挑战。受经典边缘和角点检测启发，本文提出将定向边界框的方向表示为结构张量。该表示结合了高斯方法与角度编码方案的优势，提供一种简单高效、对角度周期性鲁棒且无需额外超参数的方法。在五个数据集上的广泛评估表明，所提方法在全监督与弱监督任务中均优于以往方法，实现高精度角度预测且计算开销极低。本工作确立了结构张量作为定向目标检测中方向编码的稳健且模块化替代方案。代码已公开，便于集成至现有检测器。
原文摘要 · Abstract (English)
Oriented object detection predicts orientation in addition to object location and bounding box. Precisely predicting orientation remains challenging due to angular periodicity, which introduces boundary discontinuity issues and symmetry ambiguities. Inspired by classical works on edge and corner detection, this paper proposes to represent orientation in oriented bounding boxes as a structure tensor. This representation combines the strengths of Gaussian-based methods and angle-coder solutions, providing a simple yet efficient approach that is robust to angular periodicity issues without additional hyperparameters. Extensive evaluations across five datasets demonstrate that the proposed structure tensor representation outperforms previous methods in both fully-supervised and weakly supervised tasks, achieving high precision in angular prediction with minimal computational overhead. Thus, this work establishes structure tensors as a robust and modular alternative for encoding orientation in oriented object detection. We make our code publicly available, allowing for seamless integration into existing object detectors.
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