Few-Shot Object Detection on Remote Sensing Images

Abstract

In this article, we deal with the problem of object detection on remote sensing images. Previous researchers have developed numerous deep convolutional neural network (CNN)-based methods for object detection on remote sensing images, and they have reported remarkable achievements in detection performance and efficiency. However, current CNN-based methods often require a large number of annotated samples to train deep neural networks and tend to have limited generalization abilities for unseen object categories. In this article, we introduce a metalearning-based method for few-shot object detection on remote sensing images where only a few annotated samples are needed for the unseen object categories. More specifically, our model contains three main components: a metafeature extractor that learns to extract metafeature maps from input images, a feature reweighting module that learns class-specific reweighting vectors from the support images and use them to recalibrate the metafeature maps, and a bounding box prediction module that carries out object detection on the reweighted feature maps. We build our few-shot object detection model upon the YOLOv3 architecture and develop a multiscale object detection framework. Experiments on two benchmark data sets demonstrate that with only a few annotated samples, our model can still achieve a satisfying detection performance on remote sensing images, and the performance of our model is significantly better than the well-established baseline models.