HiCAL: Hierarchical Consistency-Based Active Learning for Drone-View Object Detection
Xingyu Zhang, Yongxu Liu, Aobo Li, Kun Han, Qinghang Zhao, Jinjian Wu · IEEE Transactions on Geoscience and Remote Sensing · 2025
The recent years have witnessed the great progress of drone-view object detection in both economic and military applications. Generally, the good performance of drone-view object detection requires a large amount of annotated data, which has imposed significant demands on human and material resources. To optimize the labelling expenses, previous work has introduced active learning to select the most valuable samples for annotation, and balances the annotation cost and model performance. However, existing active learning methods are primarily controlled by the “absolute” prediction of the model (e.g., the predicted categories for diversity, and the classification confidence for uncertainty). It would be highly misleading when the model outputs wrong prediction but with high confidence. This confident misleading is more severe in drone-view object detection as the targets are captured with varied viewpoints, illumination conditions, and possible occlusion. In this paper, we refresh the active learning with Perturbation Consistency Test (PCT), which transforms the absolute prediction into the relative error to address the situation where the absolute prediction is unreliable. The basic idea is to test the prediction consistency when the input samples are with/without perturbation, and regards the inconsistency as a measurement of the model’s resilience to guide the active selection. To this end, a Hierarchical Consistency-based Active Learning (HiCAL) is built, which constructs adversarially pair-wise inputs with hierarchical perturbation. The samples are perturbed with multi-granularity (i.e., pixel level, feature level, and object level) and afterwards, the entropy difference of the paired outputs before/after perturbation is calculated as the measurement. The samples with high difference are selected to follow a standard active learning loop. Experimental results show that HiCAL can achieve superior performance in different datasets and is easy to adapt to various types of object detectors. The code will be available on: https://github.com/zstar1003/HiCAL.