The growing demand for oriented object detection (OOD) across various domains has driven significant research in this area. However, the high cost of dataset annotation remains a major concern. Current mainstream OOD algorithms can be mainly categorized into three types: (1) fully supervised methods using complete oriented bounding box (OBB) annotations, (2) semi-supervised methods using partial OBB annotations, and (3) weakly supervised methods using weak annotations such as horizontal boxes or points. However, these algorithms inevitably increase the cost of models in terms of annotation speed or annotation cost. To address this issue, we propose: (1)the first Partial Weakly-Supervised Oriented Object Detection (PWOOD) framework based on partially weak annotations (horizontal boxes or single points), which can efficiently leverage large amounts of unlabeled data, significantly outperforming weakly supervised algorithms trained with partially weak annotations, also offers a lower cost solution; (2) Orientation-and-Scale-aware Student (OS-Student) model capable of learning orientation and scale information with only a small amount of orientation-agnostic or scale-agnostic weak annotations; and (3) Class-Agnostic Pseudo-Label Filtering strategy (CPF) to reduce the model's sensitivity to static filtering thresholds. Comprehensive experiments on DOTA-v1.0/v1.5/v2.0 and DIOR datasets demonstrate that our PWOOD framework performs comparably to, or even surpasses traditional semi-supervised algorithms. Our code will be made publicly available.

翻译：随着定向目标检测（OOD）在各个领域的需求日益增长，该领域的研究取得了显著进展。然而，数据集标注的高昂成本仍然是一个主要问题。当前主流的OOD算法主要可分为三类：（1）使用完整定向边界框（OBB）标注的全监督方法；（2）使用部分OBB标注的半监督方法；（3）使用水平框或点等弱标注的弱监督方法。然而，这些算法在标注速度或标注成本方面不可避免地增加了模型的开销。为解决这一问题，我们提出：（1）首个基于部分弱标注（水平框或单点）的部分弱监督定向目标检测（PWOOD）框架，该框架能够高效利用大量未标注数据，其性能显著优于使用部分弱标注训练的弱监督算法，同时提供了成本更低的解决方案；（2）方向与尺度感知学生（OS-Student）模型，该模型仅需少量方向无关或尺度无关的弱标注即可学习方向和尺度信息；（3）类别无关的伪标签过滤策略（CPF），以降低模型对静态过滤阈值的敏感性。在DOTA-v1.0/v1.5/v2.0和DIOR数据集上的综合实验表明，我们的PWOOD框架与传统半监督算法性能相当，甚至更优。我们的代码将公开提供。