A lane detection network based on IBN and attention

  • Wenhui Li
  • Feng QuEmail author
  • Jialun Liu
  • Fengdong Sun
  • Ying Wang


In intelligent transportation system and advanced driving assistant system, lane detection is an indispensable security link. At present, deep learning has been applied to the task of lane detection, and some methods used semantic segmentation to separate lanes from background. This paper presents a modified encoder-decoder network with instance-batch normalization net (IBN-NET) and attention mechanism based on LaneNet structure. In view of the shortcomings of batch normalization (BN) in capturing texture in end-to-end segmentation, we consider further optimizing this part from the idea of image style transfer, which we solve the problem by replacing pixel-wise classification for scene labeling with capturing content images structure. To take advantage of visual and appearance invariance of instance normalization in encoder stage, IBN layers are applied to replace normal BN layers. Secondly, attention mechanism is added to the network, forcing it to focus on lane regions. This structure is very suitable for two-class semantics segmentation task with only lane and background. The experimental results show that the method can improve detection effect.


Lane detection Deep learning CNN IBN-net Attention 



The work described in this paper was funded by Science and Technology Development Plan of Jilin Province (20170204020GX) and National Natural Science Foundation of China under grant U1564211 and 51805203.

Compliance with ethical standards

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. 1.
    Aly M (2008) Real time detection of lane markers in urban streets. In: IEEE Intelligent Vehicles Symposium, Proceedings pp 7–12Google Scholar
  2. 2.
    Azimi SM, Fischer P, Körner M, Reinartz P (2018) Aerial LaneNet: lane marking semantic segmentation in aerial imagery using wavelet-enhanced cost-sensitive symmetric fully convolutional neural networksGoogle Scholar
  3. 3.
    Chen L-C, Yang Y, Wang J et al (2016) Attention to scale: scale-aware semantic image segmentation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE pp 3640–3649Google Scholar
  4. 4.
    De Brabandere B, Neven D, Van Gool L (2017) Semantic instance segmentation with a discriminative loss functionGoogle Scholar
  5. 5.
    Ghafoorian M, Nugteren C, Baka N et al (2018) EL-GAN: embedding loss driven generative adversarial networks for lane detectionGoogle Scholar
  6. 6.
    Gopalan R, Hong T, Shneier M, Chellappa R (2012) A learning approach towards detection and tracking of lane markings. IEEE Trans Intell Transp Syst 13:1088–1098. CrossRefGoogle Scholar
  7. 7.
    He B, Ai R, Yan Y, Lang X (2016) Accurate and robust lane detection based on dual-view convolutional neutral network. In: IEEE Intelligent Vehicles Symposium, ProceedingsGoogle Scholar
  8. 8.
    Huang X, Liu M-Y, Belongie S, Kautz J (2018) Multimodal unsupervised image-to-image translationGoogle Scholar
  9. 9.
    Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017Google Scholar
  10. 10.
    Kim J, Park C (2017) End-to-end ego lane estimation based on sequential transfer learning for self-driving cars. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, pp 1194–1202Google Scholar
  11. 11.
    Lee S, Kim J, Yoon JS et al (2017) VPGNet: vanishing point guided network for lane and road marking detection and recognition. In: Proceedings of the IEEE International Conference on Computer VisionGoogle Scholar
  12. 12.
    Liu S, Cheng X, Fu W et al (2014) Numeric characteristics of generalized M-set with its asymptote. Appl Math Comput 243:767–774. MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Liu X, Deng Z, Yang G (2017a) Drivable road detection based on dilated FPN with feature aggregation. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, pp 1128–1134Google Scholar
  14. 14.
    Liu S, Pan Z, Song H (2017b) Digital image watermarking method based on DCT and fractal encoding. IET Image Process 11:815–821. CrossRefGoogle Scholar
  15. 15.
    Liu G, Liu S, Muhammad K et al (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access 6:29283–29296. CrossRefGoogle Scholar
  16. 16.
    Meyer A, Salscheider NO, Orzechowski PF, Stiller C (2018) Deep semantic lane segmentation for mapless driving. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’2018). Madrid, Spain, pp 871–875Google Scholar
  17. 17.
    Neven D, De Brabandere B, Georgoulis S et al (2018) Towards end-to-end lane detection: an instance segmentation approachGoogle Scholar
  18. 18.
    Niu J, Lu J, Xu M et al (2016) Robust lane detection using two-stage feature extraction with curve fitting. Pattern Recogn 59:225–233CrossRefGoogle Scholar
  19. 19.
    Oliveira GL, Burgard W, Brox T (2016) Efficient deep models for monocular road segmentation. In: IEEE International Conference on Intelligent Robots and SystemsGoogle Scholar
  20. 20.
    Ozgunalp U, Fan R, Ai X, Dahnoun N (2016) Multiple lane detection algorithm based on novel dense vanishing point estimation. IEEE Trans Intell Transp Syst 18:621–632CrossRefGoogle Scholar
  21. 21.
    Pan X, Shi J, Luo P et al (2017) Spatial As Deep: Spatial CNN for Traffic Scene UnderstandingGoogle Scholar
  22. 22.
    Pan X, Luo P, Shi J, Tang X (2018) Two at once: enhancing learning and generalization capacities via IBN-Net. In: The European Conference on Computer Vision (ECCV). Springer, Cham, Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds), pp 484–500CrossRefGoogle Scholar
  23. 23.
    Paszke A, Chaurasia A, Kim S, Culurciello E (2016) ENet: a deep neural network architecture for real-time semantic segmentationGoogle Scholar
  24. 24.
    Piao J, Shin H (2017) Robust hypothesis generation method using binary blob analysis for multi-lane detection. IET Image Process 11:1210–1218. CrossRefGoogle Scholar
  25. 25.
    Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. CrossRefGoogle Scholar
  26. 26.
    Song W, Yang Y, Fu M et al (2018) Lane detection and classification for forward collision warning system based on stereo vision. IEEE Sens J PP:1Google Scholar
  27. 27.
    Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylizationGoogle Scholar
  28. 28.
    Woo S, Park J, Lee J-Y, So Kweon I (2018) CBAM: convolutional block attention module. In: The European Conference on Computer Vision (ECCV)Google Scholar
  29. 29.
    Xing Y, Lv C, Chen L et al (2018) Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision. IEEE/CAA J Autom Sin 5:645–661. CrossRefGoogle Scholar
  30. 30.
    Xu K, Ba J, Kiros R et al (2015) Show, attend and tell: neural image caption generation with visual attention. In: the 32nd International Conference on Machine Learning. Lille, France, pp 2048–2057Google Scholar
  31. 31.
    Ye YY, Hao XL, Chen HJ (2018) Lane detection method based on lane structural analysis and CNNs. IET Intell Transp Syst 12:513–520. CrossRefGoogle Scholar
  32. 32.
    Yin W, Schütze H, Xiang B, Zhou B (2016) ABCNN: attention-based convolutional neural network for modeling sentence pairs. Trans Assoc Comput Linguist 4:259–272CrossRefGoogle Scholar
  33. 33.
    Yu G, Wang Z, Wu X et al (2017) Efficient lane detection using deep lane feature extraction method. SAE Int J Passeng Cars Electron Electr Syst 11. doi:
  34. 34.
    Zhang H, Goodfellow I, Metaxas D, Odena A (2018) Self-attention generative adversarial networksGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina

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