Multi-lane line detection and tracking network based on spatial semantics segmentation

被引：1
|
作者：
Shi J. [1 ]
Zhang X. [1 ]
机构：
[1] School of Mechanical and Automobile Engineering, Shanghai University Of Engineering Science, Shanghai
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2023年 / 31卷 / 09期
关键词：
lane line detection tracking; lightweight convolution neural network; machine vision; semantic segmentation; spatial convolution neural network;
D O I：
10.37188/OPE.20233109.1357
中图分类号：
学科分类号：
摘要：
Target detection networks based on deep learning has some problems in the field of lane line recognition，such as unclear lane differences，low recognition accuracy，a high false detection rate，and a high missed detection rate. To solve the aforementioned problems，a lightweight lane detection and tracking network，SCNNLane，based on spatial instance segmentation，was proposed. In the coding part，the VGG16 network and the spatial convolution neural network（SCNN）were applied to improve the ability of the network structure to learn spatial relationships，which solved the problems of blurring and discontinuity in lane prediction. Simultaneously，based on LaneNet，two branch tasks after encoding the output were coupled to improve poor foreground and background recognition and indistinguishability between lanes. Finally，the method was compared with five other semantic segmentation-based lane-line algorithms by using the TuSimple dataset. Experimental results show that the accuracy of this algorithm is 97. 12%，and the false detection rate and missed detection rate are reduced by 44. 87% and 12. 7% respectivel，as compared with LaneNet，thus meeting the demand of real-time lane line detection. © 2023 Chinese Academy of Sciences. All rights reserved.
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页码：1357 / 1365
页数：8
相关论文
共 17 条
  • [1] TOROYAN T., Global status report on road safety ［J］, Injury Prevention, 15, 4, (2009)
  • [2] TANDON S，, Et al., An Empirical Evaluation of Deep Learning on Highway Driving［EB/OL］, (2015)
  • [3] LERNER R, Et al., Recent progress in road and lane detection：a survey ［J］, Machine Vision and Applications, 25, 3, pp. 727-745, (2014)
  • [4] HE X G, JIANG L, LUO Y P，, Et al., Design of lane detection algorithm based on Hough transform ［J］, Agricultural Equipment & Vehicle Engineering, 57, 11, pp. 90-91, (2019)
  • [5] MA Q J，, HE Z CH, LIN B Y，, Et al., Long distance lane detection based on image processing［J］, Henan Science and Technology, 29, pp. 111-113, (2019)
  • [6] HINTON G E., ImageNet classification with deep convolutional neural networks［C］, Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, pp. 1097-1105, (2012)
  • [7] FERGUS R., Visualizing and Understanding Convolutional Networks ［EB/OL］, (2013)
  • [8] CUI W Q, WANG Y J，, KANG S Q，, Et al., Highway lane line detection method based on improved YOLOv3 algorithm［J］, Journal of Automation, 48, 6, pp. 1560-1568, (2022)
  • [9] WANG R X, ZHANG J L., Cross-depth convolutional feature enhancement algorithm for object detection［J］, Computer Engineering and Design, 41, 7, pp. 1864-1871, (2020)
  • [10] SHI J P，, LUO P，, Et al., Spatial as deep：spatial CNN for traffic scene understanding ［J］, Proceedings of the AAAI Conference on Artificial Intelligence, 32, 1, (2018)
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