Active Learning for Single-Stage Object Detection in UAV Images

Unmanned aerial vehicles (UAVs) are widely used for image acquisition in various applications, and object detection is a crucial task for UAV imagery analysis. However, training accurate object detectors requires a large amount of annotated data, which can be expensive and time-consuming. To address this issue, we propose an active learning framework for single-stage object detectors in UAV images. First, we introduce Diverse Uncertainty Aggregation (DUA), a novel uncertainty aggregation method that aims to select images with a more diverse variety of object classes with high uncertainties. Second, we address the problem of class imbalance by adjusting the uncertainty calculation based on the performance of each class. Third, we illustrate how reducing the number of images for labeling does not necessarily lead to a lower labeling cost. Evaluation of our approach on a common UAV dataset shows that we can perform similarly (within 0.02 0.5mAP) to using the whole dataset while using only 25% of the images and 32% of the labeled objects. It also outperforms Random Selection and some other aggregation methods. Evaluation on VOC2012 show also consistent results utilizing only 25% of the labeling cost to reach a performance within 0.1 0.5mAP of using the whole dataset. Our results suggest that our proposed active learning framework can effectively reduce the annotation cost while improving the performance of singlestage object detectors in UAV image settings. The code is available on: https://github.com/asmayamani/DUA