An oriented anchor-free object detector including feature fusion and foreground enhancement for remote sensing images

Abstract

ABSTRACT Anchor-based methods, which require a large number of pre-set anchors, have been widely used for oriented object detection in remote sensing images. However, the definitions of anchors’ sizes, aspect ratios and quantities are heuristic and the use of anchors is time-consuming. In this paper, we propose an oriented one-stage anchor-free detector for aerial image object detection. Arbitrary oriented object detection is based on oriented bounding box regression. By adaptively fusing the features from the neighbour layers of the feature pyramid network (FPN), a finer adaptive feature fusion network is proposed to align the features with ground truths. The proposed network can avoid the ambiguous heuristic-guided feature selection caused by scale variations of aerial image objects. We also design a foreground enhancement module to obtain more discriminative features from the fused FPN. Experiments on remote sensing image public datasets show that our method can outperform current one-stage anchor-free methods and achieve comparable performance with state-of-the-art two-stage anchor-based methods.