Minimizing Sample Redundancy for Label-Efficient Object Detection in Aerial Images

Objects in aerial images tend to be densely scattered and appear in arbitrary orientations, making the annotation process quite costly. To reduce the annotation cost, existing methods propose randomly annotating a proportion of images or objects for aerial object detection with fewer label usage. These approaches, however, can lead to redundancy in labels and inherit the biases associated with the imbalance in datasets. To minimize sample redundancy and alleviate data imbalance, we propose a novel labeling pattern that acquires heterogeneous object labels in a class-orthogonal manner, preserving a broader diversity of samples for each category with less annotation effort. To improve data utility, we design a Dynamic Multi-View Learning (DML) strategy to overcome the sample quantity-quality dilemma in current pseudo-labeling methods—a high pseudo-label threshold reduces sample quantity, while low thresholds compromise sample quality. First, DML separates model predictions into multiple hierarchies for finer screening, mitigating the suppression of unlabeled objects in binary pseudo-label strategies. With this separation, DML learns to construct a new view by injecting high-quality samples and masking low-quality regions in this view, simultaneously expanding sample quantity while ensuring sample quality. Unlike previous methods that mine pseudo labels solely from unlabelled regions, DML releases this constraint by learning to expand high-quality samples with a dynamic view. Extensive experiments on five benchmark datasets validate our method’s state-of-the-art accuracy and label efficiency. Notably, with approximately 5% DOTA-v2.0 annotations, DML achieves nearly 90% of the fully supervised performance. The codes will be available at https://github.com/ZhangRuixiang-WHU/ALOD_DML/.