Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://github.com/HamPerdredes/SOOD.

翻译：半监督目标检测（Semi-Supervised Object Detection, SSOD）旨在利用未标注数据提升目标检测器性能，近年已成为一项活跃的研究任务。然而，现有SSOD方法主要关注水平目标，尚未探索航拍图像中常见的多朝向目标。本文提出一种基于主流伪标签框架的新型半监督定向目标检测模型，名为SOOD。为适应航拍场景中的定向目标，我们设计了两种损失函数以提供更优监督。第一种损失聚焦目标朝向，通过基于伪标签-预测对（包含预测结果及其对应伪标签）的朝向差距自适应加权，正则化每对数据间的一致性。第二种损失聚焦图像布局，显式建模伪标签集合与预测集合之间的多对多关系，并正则化二者相似性。这种全局一致性约束可进一步促进半监督学习。实验表明，在训练过程中采用所提出的两种损失函数后，SOOD在DOTA-v1.5基准测试的多种设置下均超越了现有最优SSOD方法。代码将发布于https://github.com/HamPerdredes/SOOD。