FRPNet: A Feature-Reflowing Pyramid Network for Object Detection of Remote Sensing Images

As a significant and fundamental task in the remote sensing field, object detection has received increasing attention and research studies. However, geospatial object detection is still a challenge owing to the dramatic variation in object scales, intraclass differences, and interclass similarity from multiscale and multiclass objects. To deal with these problems, an end-to-end feature-reflowing pyramid network (FRPNet) is proposed in this letter. FRPNet has two advantages that contribute to improve object detection accuracy. First, we embed a nonlocal block into the backbone in order to get the relevancy between different regions of the geospatial image for obtaining discriminative features. Furthermore, a feature-reflowing pyramid structure is proposed to generate high-quality feature presentation for each scale through fusing fine-grained features from the adjacent lower level, which improves the detection capability for multiscale and multiclass objects. Experiments on a public remote sensing data set DIOR illustrate that FRPNet can significantly improve the performance when compared to several state-of-the-art detection approaches in terms of mean average precision (mAP).