Semi-Supervised Object Detection Framework with Object First Mixup for Remote Sensing Images

This paper proposes a Simple Semi-supervised Object Detection framework for Remote Sensing images, which is named SSOD-RS. SSOD-RS contains two parts, improved self-training and consistency regularization based on strong data augmentations with improved mixup. Firstly, as an augmentation algorithm, Object First mixup (OF-mixup) is proposed to adjust the weight of objects and the background, which expands the distribution of training samples while reducing the interference of the remote sensing complex background to the features of objects. Secondly, the strategy of training with assembling loss and fine-tuning is introduced into self-training to make the model fit the feature distribution of the true-labels after learning the features from the pseudo-labels. Experimental results demonstrate that SSOD-RS making use of unlabeled images can significantly improve the accuracy of the model.