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An Orientation-Aware Anchor-Free Detector for Aerial Object Detection

Most aerial object detectors mainly adopt anchor-based methods, which require manually designed rough anchors. However, these anchors usually contain background redundancy unrelated to the objects, decreasing detection performance and introducing additional calculations. In this paper, we propose an orientation-aware anchor-free network (OAF-Net) for object detection. OAF-Net first produces coarse oriented boxes by coarse location network based on pixel-level regression and then refines them with aligned features. In particular, we also apply a new metric to measure the orientation-aware center-ness, which is a weighting strategy of positive samples to reduce the different contributions of the low-quality detection boxes. Experiments on the DOTA1.0 and HRSC2016 datasets reveal that OAF-Net outperforms the state-of-the-art in terms of comprehensive detection performance for various objects with different scales and orientations in aerial images.