Abstract
Weakly supervised oriented object detection (WS-OOD) has gained attention as a cost-effective alternative to fully supervised methods, providing both efficiency and high accuracy. Among weakly supervised approaches, horizontal bounding box (HBox)-supervised OOD stands out for its ability to directly leverage existing HBox annotations while achieving the highest accuracy under weak supervision settings. This paper introduces adaptive bounding box scaling and symmetry-prior-based orientation prediction, called ABBSPO, a framework for WS-OOD. Our ABBSPO addresses limitations of previous HBox-supervised OOD methods, which compare ground truth (GT) HBoxes directly with the minimum circumscribed rectangles of predicted RBoxes, often leading to inaccurate scale estimation. To overcome this, we propose: (i) Adaptive Bounding Box Scaling (ABBS), which appropriately scales GT HBoxes to optimize for the size of each predicted RBox, ensuring more accurate scale prediction; and (ii) a Symmetric Prior Angle (SPA) loss that exploits inherent symmetry of aerial objects for self-supervised learning, resolving issues in previous methods where learning collapses when predictions for all three augmented views (original, rotated, and flipped) are consistently incorrect. Extensive experimental results demonstrate that ABBSPO achieves state-of-the-art performance, outperforming existing methods.
Abstract (translated)
弱监督定向目标检测（WS-OOD）作为一种成本效益高的完全监督方法的替代方案，因其效率和高精度而受到关注。在各种弱监督方法中，水平边界框（HBox）监督定向目标检测因能够直接利用现有的HBox注释，并在弱监督设置下达到最高准确性而脱颖而出。本文提出了一种名为ABBSPO的框架，该框架结合了自适应边界框缩放和基于对称先验的角度预测，以解决WS-OOD中的问题。 我们的ABBSPO方法解决了之前HBox监督定向目标检测方法中存在的限制，这些方法直接比较地面真实（GT）HBoxes与预测矩形框（RBoxes）的最小外接矩形，通常会导致不准确的比例估计。为了解决这个问题，我们提出了以下两个方案： (i) 自适应边界框缩放 (ABBS)，该方案适当地调整GT HBox以优化每个预测RBox的大小，从而确保更精确的比例预测； (ii) 对称先验角度（SPA）损失，利用空中物体固有的对称性进行自监督学习，解决了先前方法中当三个增强视图（原始、旋转和翻转）的所有预测均不正确时导致的学习崩溃问题。 通过广泛的实验结果表明，ABBSPO在WS-OOD任务上实现了最先进的性能，并且超过了现有的所有方法。
URL
https://arxiv.org/abs/2512.10031
