Various object detection techniques are employed on drone platforms. However, the task of annotating drone-view samples is both time-consuming and laborious. This is primarily due to the presence of numerous small-sized instances to be labeled in the drone-view image. To tackle this issue, we propose HALD, a hierarchical active learning approach for low-altitude drone-view object detection. HALD extracts unlabeled image information sequentially from different levels, including point, box, image, and class, aiming to obtain a reliable indicator of image information. The point-level module is utilized to ascertain the valid count and location of instances, while the box-level module screens out reliable predictions. The image-level module selects candidate samples by calculating the consistency of valid boxes within an image, and the class-level module selects the final selected samples based on the distribution of candidate and labeled samples across different classes. Extensive experiments conducted on the VisDrone and CityPersons datasets demonstrate that HALD outperforms several other baseline methods. Additionally, we provide an in-depth analysis of each proposed module. The results show that the performance of evaluating the informativeness of samples can be effectively improved by the four hierarchical levels.

无人机平台采用了各种物体检测技术。然而，注释无人机视图样本的任务既费时又费力。这主要是由于无人机视图图像中存在大量要标记的小型实例。为了解决这个问题，我们提出了 HALD，一种用于低空无人机视图对象检测的分层主动学习方法。 HALD从点、框、图像、类等不同层次依次提取未标记的图像信息，旨在获得图像信息的可靠指标。点级模块用于确定实例的有效计数和位置，而框级模块则筛选出可靠的预测。图像级模块通过计算图像内有效框的一致性来选择候选样本，类级模块根据候选样本和标记样本在不同类中的分布来选择最终选定的样本。在 VisDrone 和 CityPersons 数据集上进行的大量实验表明，HALD 优于其他几种基线方法。此外，我们还对每个建议的模块进行了深入分析。结果表明，四个层次可以有效提高样本信息量评价的性能。