Hierarchical Active Learning for Low-Altitude Drone-View Object Detection

Abstract

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.