Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching
Boxuan Zhang, Zengmao Wang, Bo Du
Abstract
The lack of object-level annotations poses a significant challenge for object detection in remote sensing images. To address this issue, active learning and semi-supervised learning techniques have been proposed to enhance the quality and quantity of annotations. Active learning focuses on selecting the most informative samples for annotation, while semi-supervised learning leverages the knowledge from unlabeled samples. In this paper, we propose a novel active learning method to boost semi-supervised object detection for remote sensing images with a teacher-student network, called SSOD-AT. The proposed method incorporates a RoI Comparison module (RoICM) to generate high-confidence pseudo-labels for Regions of Interest (RoIs). Meanwhile, the RoICM is utilized to identify the top-K uncertain images. To reduce redundancy in the top-K uncertain images for human labeling, a diversity criterion is introduced based on object-level prototypes of different categories using both labeled and pseudo-labeled images. Extensive experiments on DOTA and DIOR two popular datasets demonstrate that our proposed method outperforms state-of-the-art methods for object detection in remote sensing images. Compared with the best performance in the SOTA methods, the proposed method achieves 1% improvement at most cases in the whole active learning.