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\bibitem{detr}
N.~Carion, F.~Massa, G.~Synnaeve, N.~Usunier, A.~Kirillov, and S.~Zagoruyko,
  ``End-to-end object detection with transformers,'' in \emph{European
  conference on computer vision}.\hskip 1em plus 0.5em minus 0.4em\relax
  Springer, 2020, pp. 213--229.

\bibitem{learnNMS}
J.~Hosang, R.~Benenson, and B.~Schiele, ``Learning non-maximum suppression,''
  in \emph{Proceedings of the IEEE conference on computer vision and pattern
  recognition}, 2017, pp. 4507--4515.

\bibitem{yolov10}
A.~Wang, H.~Chen, L.~Liu, K.~Chen, Z.~Lin, J.~Han, and G.~Ding, ``Yolov10:
  Real-time end-to-end object detection,'' \emph{arXiv preprint
  arXiv:2405.14458}, 2024.

\bibitem{o2o}
P.~Sun, Y.~Jiang, E.~Xie, W.~Shao, Z.~Yuan, C.~Wang, and P.~Luo, ``What makes
  for end-to-end object detection?'' in \emph{International Conference on
  Machine Learning}.\hskip 1em plus 0.5em minus 0.4em\relax PMLR, 2021, pp.
  9934--9944.

\bibitem{o3d}
J.~Wang, L.~Song, Z.~Li, H.~Sun, J.~Sun, and N.~Zheng, ``End-to-end object
  detection with fully convolutional network,'' in \emph{Proceedings of the
  IEEE/CVF conference on computer vision and pattern recognition}, 2021, pp.
  15\,849--15\,858.

\bibitem{relationnet}
H.~Hu, J.~Gu, Z.~Zhang, J.~Dai, and Y.~Wei, ``Relation networks for object
  detection,'' in \emph{Proceedings of the IEEE conference on computer vision
  and pattern recognition}, 2018, pp. 3588--3597.

\bibitem{yolact}
D.~Bolya, C.~Zhou, F.~Xiao, and Y.~J. Lee, ``Yolact: Real-time instance
  segmentation,'' in \emph{Proceedings of the IEEE/CVF international conference
  on computer vision}, 2019, pp. 9157--9166.

\bibitem{iouloss}
J.~Yu, Y.~Jiang, Z.~Wang, Z.~Cao, and T.~Huang, ``Unitbox: An advanced object
  detection network,'' in \emph{Proceedings of the 24th ACM international
  conference on Multimedia}, 2016, pp. 516--520.

\bibitem{giouloss}
H.~Rezatofighi, N.~Tsoi, J.~Gwak, A.~Sadeghian, I.~Reid, and S.~Savarese,
  ``Generalized intersection over union: A metric and a loss for bounding box
  regression,'' in \emph{Proceedings of the IEEE/CVF conference on computer
  vision and pattern recognition}, 2019, pp. 658--666.

\bibitem{clrnet}
T.~Zheng, Y.~Huang, Y.~Liu, W.~Tang, Z.~Yang, D.~Cai, and X.~He, ``Clrnet:
  Cross layer refinement network for lane detection,'' in \emph{Proceedings of
  the IEEE/CVF conference on computer vision and pattern recognition}, 2022,
  pp. 898--907.

\bibitem{adnet}
L.~Xiao, X.~Li, S.~Yang, and W.~Yang, ``Adnet: Lane shape prediction via anchor
  decomposition,'' in \emph{Proceedings of the IEEE/CVF International
  Conference on Computer Vision}, 2023, pp. 6404--6413.

\bibitem{date}
Y.~Chen, Q.~Chen, Q.~Hu, and J.~Cheng, ``Date: Dual assignment for end-to-end
  fully convolutional object detection,'' \emph{arXiv preprint
  arXiv:2211.13859}, 2022.

\bibitem{clrernet}
H.~Honda and Y.~Uchida, ``Clrernet: improving confidence of lane detection with
  laneiou,'' in \emph{Proceedings of the IEEE/CVF Winter Conference on
  Applications of Computer Vision}, 2024, pp. 1176--1185.

\bibitem{yolox}
G.~Zheng, L.~Songtao, W.~Feng, L.~Zeming, S.~Jian \emph{et~al.}, ``Yolox:
  Exceeding yolo series in 2021,'' \emph{arXiv preprint arXiv:2107.08430},
  2021.

\end{thebibliography}