While existing semi-supervised object detection (SSOD) methods perform well in general scenes, they encounter challenges in handling oriented objects in aerial images. We experimentally find three gaps between general and oriented object detection in semi-supervised learning: 1) Sampling inconsistency: the common center sampling is not suitable for oriented objects with larger aspect ratios when selecting positive labels from labeled data. 2) Assignment inconsistency: balancing the precision and localization quality of oriented pseudo-boxes poses greater challenges which introduces more noise when selecting positive labels from unlabeled data. 3) Confidence inconsistency: there exists more mismatch between the predicted classification and localization qualities when considering oriented objects, affecting the selection of pseudo-labels. Therefore, we propose a Multi-clue Consistency Learning (MCL) framework to bridge gaps between general and oriented objects in semi-supervised detection. Specifically, considering various shapes of rotated objects, the Gaussian Center Assignment is specially designed to select the pixel-level positive labels from labeled data. We then introduce the Scale-aware Label Assignment to select pixel-level pseudo-labels instead of unreliable pseudo-boxes, which is a divide-and-rule strategy suited for objects with various scales. The Consistent Confidence Soft Label is adopted to further boost the detector by maintaining the alignment of the predicted results. Comprehensive experiments on DOTA-v1.5 and DOTA-v1.0 benchmarks demonstrate that our proposed MCL can achieve state-of-the-art performance in the semi-supervised oriented object detection task.

翻译：尽管现有的半监督目标检测方法在通用场景中表现良好，但在处理航空图像中的定向目标时仍面临挑战。我们通过实验发现了半监督学习中通用目标检测与定向目标检测之间的三个差距：1）采样不一致性：从标注数据中选择正样本标签时，常用的中心采样策略不适用于长宽比较大的定向目标。2）分配不一致性：平衡定向伪框的精度与定位质量面临更大挑战，这导致从未标注数据中选择正样本标签时引入更多噪声。3）置信度不一致性：考虑定向目标时，预测的分类质量与定位质量之间存在更严重的不匹配，影响伪标签的选择。为此，我们提出多线索一致性学习框架，以弥合半监督检测中通用目标与定向目标间的差距。具体而言，针对旋转目标的各种形状，我们专门设计了高斯中心分配策略，用于从标注数据中选择像素级正样本标签。随后引入尺度感知标签分配策略，以选择像素级伪标签而非不可靠的伪框，这是一种适用于多尺度目标的"分而治之"策略。采用一致性置信度软标签策略，通过保持预测结果的对齐性进一步提升检测器性能。在DOTA-v1.5和DOTA-v1.0基准上的综合实验表明，我们提出的MCL方法能够在半监督定向目标检测任务中达到最先进的性能水平。