MULTI-SCALE CONTEXT-AWARE R-CNN FOR FEW-SHOT OBJECT DETECTION IN REMOTE SENSING IMAGES

In the field of remote sensing image object detection, the popular CNN-based methods need a large-scale and diverse dataset that is costly, and have limited generalization abilities for new categories. The few-shot object detection can be driven using only a few annotated samples. Existing few-shot detection methods are mainly designed for natural images, which ignore multi-scale objects and complex environments in remote sensing images. To tackle these challenges, we propose a two-stage multi-scale method based on context mechanism. Guided by the context-aware module, the multi-scale contextual information around the object is effectively extract and adaptively is combined into the ROI features to enhance the classification ability of the detector, which can reduce the classification confusion. Comparative experiments on public remote sensing image dataset RSOD show the effectiveness of our method.