DAFNe

DAFNe is a dense one-stage anchor-free deep model for oriented object detection. It is a deep neural network that performs 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, it reduces the prediction complexity by refraining from employing bounding box anchors. This enables a tighter fit to oriented objects, leading to a better separation of bounding boxes especially in case of dense object distributions. Moreover, it introduces an orientation-aware generalization of the center-ness function to arbitrary quadrilaterals that takes into account the object's orientation and that, accordingly, accurately down-weights low-quality predictions