Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects. Active learning effectively reduces the data labeling cost by selectively querying the informative and representative unlabelled samples. However, existing active learning methods are mainly with class-balanced settings and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenarios due to the long-tailed class distribution and dense small objects in aerial scenes. In this paper, we propose a novel active learning method for cost-effective aerial object detection. Specifically, both object-level and image-level informativeness are considered in the object selection to refrain from redundant and myopic querying. Besides, an easy-to-use class-balancing criterion is incorporated to favor the minority objects to alleviate the long-tailed class distribution problem in model training. We further devise a training loss to mine the latent knowledge in the unlabeled image regions. Extensive experiments are conducted on the DOTA-v1.0 and DOTA-v2.0 benchmarks to validate the effectiveness of the proposed method. For the ReDet, KLD, and SASM detectors on the DOTA-v2.0 dataset, the results show that our proposed MUS-CDB method can save nearly 75\% of the labeling cost while achieving comparable performance to other active learning methods in terms of mAP.Code is publicly online (https://github.com/ZJW700/MUS-CDB).

翻译：近年来，航空目标检测模型依赖大量标注训练数据，这在密集目标的大尺度航空场景中导致难以承受的人工标注成本。主动学习通过选择性查询信息丰富且具代表性的未标注样本，有效降低了数据标注成本。然而，现有主动学习方法主要针对通用目标检测任务采用类别平衡设置和基于图像的查询策略，由于航空场景中长尾类别分布和密集小目标的存在，这些方法较难适用于航空目标检测场景。本文提出一种新颖的主动学习方法，旨在实现经济高效的航空目标检测。具体而言，在目标选择中同时考虑目标级和图像级的信息量，以避免冗余和短视的查询。此外，引入易用的类别平衡准则以支持少数类目标，从而缓解模型训练中的长尾类别分布问题。进一步设计训练损失以挖掘未标注图像区域的潜在知识。在DOTA-v1.0和DOTA-v2.0基准数据集上进行大量实验以验证所提方法的有效性。针对DOTA-v2.0数据集上的ReDet、KLD和SASM检测器，结果表明，所提MUS-CDB方法在节省近75%标注成本的同时，其平均精度均值（mAP）性能与其他主动学习方法相当。代码已开源（https://github.com/ZJW700/MUS-CDB）。