Region-Attentioned Network with Location Scoring Dynamic-Threshold NMS for Object Detection in Remote Sensing Images

Abstract

Object detection in remote sensing images is of importance in the field of computer vision. Although many advanced methods have succeeded in natural images, the progress in remote sensing images is relatively slow due to the complex backgrounds, vertical views, and variations in kind and size of the objects. To solve these problems, we propose a region-attentioned network with location scoring dynamic-threshold NMS for object detection in remote sensing images. In particular, we first introduce the saliency constraint and propose a region-attentioned network (RANet) to effectively enhance the object regions for better detection. Meanwhile, the proposed network adopts a feature pyramid, which fully uses the low-level and high-level features, to improve the ability for handling the multiscale objects. Then, considering that there are many dense objects in remote sensing images, we propose a novel dynamic-threshold NMS (DTNMS) method for overlap detection elimination, which is more reasonable and efficient than the traditional NMS method. In addition, we further employ the IoU header to obtain the location information of the predicted boxes and propose location scoring dynamic-threshold NMS (LSDTNMS), which can further improve the detection performance. Due to the prediction of the target mask in RANet, we can also obtain the detection results of the rotating bounding box. To verify the effectiveness of the proposed method, we execute comparative experiments on the remote sensing public dataset and the experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods.