  We present DAFNe, a Dense one-stage Anchor-Free deep Network for oriented
object detection. As a one-stage model, it performs bounding box predictions on
a dense grid over the input image, being architecturally simpler in design, as
well as easier to optimize than its two-stage counterparts. Furthermore, as an
anchor-free model, it reduces the prediction complexity by refraining from
employing bounding box anchors. With DAFNe we introduce an orientation-aware
generalization of the center-ness function for arbitrarily oriented bounding
boxes to down-weight low-quality predictions and a center-to-corner bounding
box prediction strategy that improves object localization performance. Our
experiments show that DAFNe outperforms all previous one-stage anchor-free
models on DOTA 1.0, DOTA 1.5, and UCAS-AOD and is on par with the best models
on HRSC2016.
