AFDet: Toward More Accurate and Faster Object Detection in Remote Sensing Images

Object detection in remote sensing imagery usually suffers from inaccurate target localization and bounding box regression uncertainty, mainly due to the varying sizes of objects and the complexity of the background. Most detectors address these challenges by adding various feature extraction modules, which increases the size and computational burden of the network. In this article, we propose a more accurate and faster detector named AFDet, which is composed of two parts: a backbone pretrained on ImageNet and a head that includes a center prediction branch (CPB), semantic supervision branch (SSB), and boundary estimation branch (BEB). CPB produces a keypoint heatmap using an elliptical Gaussian kernel to adapt to the ground truth with a large aspect ratio. SSB, which is used only during training, extracts extra keypoint features from boundary and interior points rather than only from the center point, thereby improving the quality of object localization. BEB predicts the distributions of the bounding box in four directions, which is further supervised by the focus loss, and the gather loss raises the box prediction accuracy. To verify the effectiveness and robustness of AFDet, we conduct extensive experiments on three widely used optical remote sensing object detection datasets, i.e., NWPU VHR-10, DIOR, and HRRSD, for which AFDet achieves state-of-the-art results.

Automated summary

In this article, a more accurate and faster detector named AFDet is proposed for object detection in remote sensing imagery. AFDet consists of a backbone pretrained on ImageNet and a head that includes a center prediction branch, semantic supervision branch, and boundary estimation branch, achieving state-of-the-art results on widely used optical remote sensing object detection datasets.