王凯博士的论文《Global Focal Learning for Semi-Supervised Oriented Object Detection》被一区SCI期刊EEE Transaction on Geoscience and Remote Sensing录用

Oriented object detectors have achieved great success in aerial detection tasks with the help of ample labeled data. Unlabeled images are easier and less expensive to obtain than labeled aerial images. Therefore, semi-supervised oriented object detection (SSOOD) is becoming a hot task, which can leverage both labeled and unlabeled data to train oriented detectors. Most SSOOD approaches focus on well-designed approaches to generate high-quality pseudo labels (PLs) or positive learning regions, which are limited to complex and variable aerial scenes. This study first analyzes key factors influencing the performance of SSOOD and proposes a global focal learning method (termed as focal teacher) without artificial priori design. It relies on global region and soft regression approaches to blur the boundaries between positive and negative samples, mainly through localization focal loss to achieve. It leverages the localization consistency between the teacher and student model to focus more on hard regions. Moreover, we organize a large remote sensing unlabeled (RSUL) dataset to exploit the performance potential of oriented detectors on mainstream aerial detection datasets (DOTA and DIOR). Adequate experiments reveal that the proposed method achieves the best performance compared with other mainstream SSOOD methods, including partly, fully, and additional data settings on DOTA and DIOR datasets. Semi-supervised mechanisms without preset learning regions can be better applied in dense and complex aerial scenes.

Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 62)

Article Sequence Number: 5636013

Date of Publication: 05 August 2024

ISSN Information:

DOI: 10.1109/TGRS.2024.3438844