Simple, Fast, Accurate Object Detection based on Anchor-Free Method for High Resolution Remote Sensing Images

Abstract

On object detection of remote sensing images, speed and accuracy are considered equally significant for time-sensitive tasks. Traditional object detection methods of remote sensing images use anchors to regress the bounding boxes and the position of objects which quite limit the speed of detection. In this paper, we make an attempt at employing an anchor-free method named CenterNet to address the problem of object detection for remote sensing images. This model computes the key point to regress the location, local offset and size without enumerating a nearly exhaustive list of potential object locations and size. Thus CenterNet is simpler, faster, more accurate and end-to-end differentiable than other anchor-based methods. We evaluate the performance of Cen-terNet with various backbones on remote sensing images dataset NWPU VHR-10. Among them, CenterNet based on DLA-34 backbone achieves the highest accuracy of 95.7% on the test set, which significantly outperforms most state-of-the-art methods while maintaining a real-time prediction speed of 31 FPS on NVIDIA GTX 1080. Analysis on experimental results demonstrates that this anchor-free network achieves a better balance between accuracy and speed for this task compared to other methods.