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.
@article{arxiv.2109.06148,
title = {DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection},
author = {Steven Lang and Fabrizio Ventola and Kristian Kersting},
journal= {arXiv preprint arXiv:2109.06148},
year = {2022}
}
Comments
Main paper: 8 pages, References: 2 pages, Appendix: 7 pages; Main paper: 6 figures, Appendix: 6 figures