polarrcnn-thesis/main.bbl

% Generated by IEEEtran.bst, version: 1.14 (2015/08/26)
\begin{thebibliography}{10}
\providecommand{\url}[1]{#1}
\csname url@samestyle\endcsname
\providecommand{\newblock}{\relax}
\providecommand{\bibinfo}[2]{#2}
\providecommand{\BIBentrySTDinterwordspacing}{\spaceskip=0pt\relax}
\providecommand{\BIBentryALTinterwordstretchfactor}{4}
\providecommand{\BIBentryALTinterwordspacing}{\spaceskip=\fontdimen2\font plus
\BIBentryALTinterwordstretchfactor\fontdimen3\font minus
  \fontdimen4\font\relax}
\providecommand{\BIBforeignlanguage}[2]{{%
\expandafter\ifx\csname l@#1\endcsname\relax
\typeout{** WARNING: IEEEtran.bst: No hyphenation pattern has been}%
\typeout{** loaded for the language `#1'. Using the pattern for}%
\typeout{** the default language instead.}%
\else
\language=\csname l@#1\endcsname
\fi
#2}}
\providecommand{\BIBdecl}{\relax}
\BIBdecl

\bibitem{cannyedge}
J.~Canny, ``A computational approach to edge detection,'' \emph{IEEE
  Transactions on pattern analysis and machine intelligence}, no.~6, pp.
  679--698, 1986.

\bibitem{houghtransform}
J.~Illingworth and J.~Kittler, ``A survey of the hough transform,''
  \emph{Computer vision, graphics, and image processing}, vol.~44, no.~1, pp.
  87--116, 1988.

\bibitem{kluge1995deformable}
K.~Kluge and S.~Lakshmanan, ``A deformable-template approach to lane
  detection,'' in \emph{Proceedings of the Intelligent Vehicles' 95.
  Symposium}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 1995, pp. 54--59.

\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{fasterrcnn}
S.~Ren, K.~He, R.~Girshick, and J.~Sun, ``Faster r-cnn: Towards real-time
  object detection with region proposal networks,'' \emph{IEEE transactions on
  pattern analysis and machine intelligence}, vol.~39, no.~6, pp. 1137--1149,
  2016.

\bibitem{laneatt}
L.~Tabelini, R.~Berriel, T.~M. Paixao, C.~Badue, A.~F. De~Souza, and
  T.~Oliveira-Santos, ``Keep your eyes on the lane: Real-time attention-guided
  lane detection,'' in \emph{Proceedings of the IEEE/CVF conference on computer
  vision and pattern recognition}, 2021, pp. 294--302.

\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{srlane}
C.~Chen, J.~Liu, C.~Zhou, J.~Tang, and G.~Wu, ``Sketch and refine: Towards fast
  and accurate lane detection,'' in \emph{Proceedings of the AAAI Conference on
  Artificial Intelligence}, vol.~38, no.~2, 2024, pp. 1001--1009.

\bibitem{tusimple}
\BIBentryALTinterwordspacing
{TuSimple}, ``Tusimple benchmark,'' 2020, accessed: September 2020. [Online].
  Available: \url{https://github.com/TuSimple/tusimple-benchmark/}
\BIBentrySTDinterwordspacing

\bibitem{scnn}
X.~Pan, J.~Shi, P.~Luo, X.~Wang, and X.~Tang, ``Spatial as deep: Spatial cnn
  for traffic scene understanding,'' in \emph{Proceedings of the AAAI
  conference on artificial intelligence}, vol.~32, no.~1, 2018.

\bibitem{llamas}
K.~Behrendt and R.~Soussan, ``Unsupervised labeled lane markers using maps,''
  in \emph{Proceedings of the IEEE/CVF international conference on computer
  vision workshops}, 2019, pp. 0--0.

\bibitem{curvelanes}
H.~Xu, S.~Wang, X.~Cai, W.~Zhang, X.~Liang, and Z.~Li, ``Curvelane-nas:
  Unifying lane-sensitive architecture search and adaptive point blending,'' in
  \emph{Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK,
  August 23--28, 2020, Proceedings, Part XV 16}.\hskip 1em plus 0.5em minus
  0.4em\relax Springer, 2020, pp. 689--704.

\bibitem{dalnet}
Z.~Yu, Q.~Liu, W.~Wang, L.~Zhang, and X.~Zhao, ``Dalnet: A rail detection
  network based on dynamic anchor line,'' \emph{IEEE Transactions on
  Instrumentation and Measurement}, 2024.

\bibitem{lanenet}
Z.~Wang, W.~Ren, and Q.~Qiu, ``Lanenet: Real-time lane detection networks for
  autonomous driving,'' \emph{arXiv preprint arXiv:1807.01726}, 2018.

\bibitem{ufld}
Z.~Qin, H.~Wang, and X.~Li, ``Ultra fast structure-aware deep lane detection,''
  in \emph{Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK,
  August 23--28, 2020, Proceedings, Part XXIV 16}.\hskip 1em plus 0.5em minus
  0.4em\relax Springer, 2020, pp. 276--291.

\bibitem{ufldv2}
Z.~Qin, P.~Zhang, and X.~Li, ``Ultra fast deep lane detection with hybrid
  anchor driven ordinal classification,'' \emph{IEEE transactions on pattern
  analysis and machine intelligence}, vol.~46, no.~5, pp. 2555--2568, 2022.

\bibitem{condlanenet}
L.~Liu, X.~Chen, S.~Zhu, and P.~Tan, ``Condlanenet: a top-to-down lane
  detection framework based on conditional convolution,'' in \emph{Proceedings
  of the IEEE/CVF international conference on computer vision}, 2021, pp.
  3773--3782.

\bibitem{fololane}
Z.~Qin, P.~Zhang, and X.~Li, ``Ultra fast deep lane detection with hybrid
  anchor driven ordinal classification,'' \emph{IEEE transactions on pattern
  analysis and machine intelligence}, vol.~46, no.~5, pp. 2555--2568, 2022.

\bibitem{ganet}
M.~Morley, R.~Atkinson, D.~Savi{\'c}, and G.~Walters, ``Ganet: genetic
  algorithm platform for pipe network optimisation,'' \emph{Advances in
  engineering software}, vol.~32, no.~6, pp. 467--475, 2001.

\bibitem{polylanenet}
L.~Tabelini, R.~Berriel, T.~M. Paixao, C.~Badue, A.~F. De~Souza, and
  T.~Oliveira-Santos, ``Polylanenet: Lane estimation via deep polynomial
  regression,'' in \emph{2020 25th International Conference on Pattern
  Recognition (ICPR)}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 2021, pp.
  6150--6156.

\bibitem{lstr}
R.~Liu, Z.~Yuan, T.~Liu, and Z.~Xiong, ``End-to-end lane shape prediction with
  transformers,'' in \emph{Proceedings of the IEEE/CVF winter conference on
  applications of computer vision}, 2021, pp. 3694--3702.

\bibitem{bezierlanenet}
Z.~Feng, S.~Guo, X.~Tan, K.~Xu, M.~Wang, and L.~Ma, ``Rethinking efficient lane
  detection via curve modeling,'' in \emph{Proceedings of the IEEE/CVF
  Conference on Computer Vision and Pattern Recognition}, 2022, pp.
  17\,062--17\,070.

\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.

\bibitem{sparse}
J.~Liu, Z.~Zhang, M.~Lu, H.~Wei, D.~Li, Y.~Xie, J.~Peng, L.~Tian, A.~Sirasao,
  and E.~Barsoum, ``Sparse laneformer,'' \emph{arXiv preprint
  arXiv:2404.07821}, 2024.

\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{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{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{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{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{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{linecnn}
X.~Li, J.~Li, X.~Hu, and J.~Yang, ``Line-cnn: End-to-end traffic line detection
  with line proposal unit,'' \emph{IEEE Transactions on Intelligent
  Transportation Systems}, vol.~21, no.~1, pp. 248--258, 2019.

\bibitem{vil100}
Y.~Zhang, L.~Zhu, W.~Feng, H.~Fu, M.~Wang, Q.~Li, C.~Li, and S.~Wang,
  ``Vil-100: A new dataset and a baseline model for video instance lane
  detection,'' in \emph{Proceedings of the IEEE/CVF international conference on
  computer vision}, 2021, pp. 15\,681--15\,690.

\bibitem{xu2022overview}
Z.-Q.~J. Xu, Y.~Zhang, and T.~Luo, ``Overview frequency principle/spectral bias
  in deep learning,'' \emph{arXiv preprint arXiv:2201.07395}, 2022.

\bibitem{stewart2016end}
R.~Stewart, M.~Andriluka, and A.~Y. Ng, ``End-to-end people detection in
  crowded scenes,'' in \emph{Proceedings of the IEEE conference on computer
  vision and pattern recognition}, 2016, pp. 2325--2333.

\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{alemi2016deep}
A.~A. Alemi, I.~Fischer, J.~V. Dillon, and K.~Murphy, ``Deep variational
  information bottleneck,'' \emph{arXiv preprint arXiv:1612.00410}, 2016.

\bibitem{focal}
T.-Y. Lin, P.~Goyal, R.~Girshick, K.~He, and P.~Doll{\'a}r, ``Focal loss for
  dense object detection,'' in \emph{Proceedings of the IEEE international
  conference on computer vision}, 2017, pp. 2980--2988.

\bibitem{pss}
Q.~Zhou and C.~Yu, ``Object detection made simpler by eliminating heuristic
  nms,'' \emph{IEEE Transactions on Multimedia}, vol.~25, pp. 9254--9262, 2023.

\bibitem{adam}
D.~P. Kingma, ``Adam: A method for stochastic optimization,'' \emph{arXiv
  preprint arXiv:1412.6980}, 2014.

\bibitem{resnet}
K.~He, X.~Zhang, S.~Ren, and J.~Sun, ``Deep residual learning for image
  recognition,'' in \emph{Proceedings of the IEEE conference on computer vision
  and pattern recognition}, 2016, pp. 770--778.

\bibitem{dla}
F.~Yu, D.~Wang, E.~Shelhamer, and T.~Darrell, ``Deep layer aggregation,'' in
  \emph{Proceedings of the IEEE conference on computer vision and pattern
  recognition}, 2018, pp. 2403--2412.

\bibitem{resa}
T.~Zheng, H.~Fang, Y.~Zhang, W.~Tang, Z.~Yang, H.~Liu, and D.~Cai, ``Resa:
  Recurrent feature-shift aggregator for lane detection,'' in \emph{Proceedings
  of the AAAI conference on artificial intelligence}, vol.~35, no.~4, 2021, pp.
  3547--3554.

\bibitem{laneaf}
H.~Abualsaud, S.~Liu, D.~B. Lu, K.~Situ, A.~Rangesh, and M.~M. Trivedi,
  ``Laneaf: Robust multi-lane detection with affinity fields,'' \emph{IEEE
  Robotics and Automation Letters}, vol.~6, no.~4, pp. 7477--7484, 2021.

\bibitem{bsnet}
H.~Chen, M.~Wang, and Y.~Liu, ``Bsnet: Lane detection via draw b-spline curves
  nearby,'' \emph{arXiv preprint arXiv:2301.06910}, 2023.

\bibitem{enetsad}
Y.~Hou, Z.~Ma, C.~Liu, and C.~C. Loy, ``Learning lightweight lane detection
  cnns by self attention distillation,'' in \emph{Proceedings of the IEEE/CVF
  international conference on computer vision}, 2019, pp. 1013--1021.

\bibitem{pointlanenet}
Z.~Chen, Q.~Liu, and C.~Lian, ``Pointlanenet: Efficient end-to-end cnns for
  accurate real-time lane detection,'' in \emph{2019 IEEE intelligent vehicles
  symposium (IV)}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 2019, pp.
  2563--2568.

\end{thebibliography}