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Real-Time Lane Detection Based on Deep Learning

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Abstract

As the research and development of autonomous vehicles has become more active, lane detection technologies for providing road information have become key elements. There are limits to detecting lanes in dynamic driving environments in conventional machine vision research, as the approaches are generally dependent on expert scenarios and fine-tuned heuristics. Deep learning has shown good performance in classifying target information with this distribution of nonlinear data; thus, many studies have actively applied deep learning to lane detection. However, most of these studies have focused on improving the accuracy, rather than on the operating speed. For the work reported herein, a benchmarking deep-learning framework for lane detection was applied with lightened feature extraction modules and decoder modules. These were used to compare performances and to present an indicator for selecting a model for optimizing real-time performance and accuracy. The VGG-16, MobileNet, and ShuffleNet networks were used for the encoder module, whereas frontend dilation and UNet were used for the decoder module. The limitations of the benchmarking framework were analyzed, and perspective loss concepts were applied to the processing of the network using front-view images to ensure improvements in the accuracy and operating speed. All of the candidate networks obtained objective performance indicators based on a large-scale benchmark dataset (TuSimple) and network training with a dataset collected and verified via performance on public roads in Singapore.

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References

  1. Chee W, Lau PY (2017) A Framework for lane departure warning system for various lane markings. In: 2017 Proceedings of international workshop on advanced image technology (IWAIT 2017), Penang, Malaysia

  2. Risack R, Mohler N, Enkelmann W (2000) A video-based lane keeping assistant. In: Proceedings of the IEEE intelligent vehicles symposium 2000 (Cat. No. 00TH8511), IEEE, pp. 356–361

  3. Suddamalla U, Kundu S, Farkade S, Das A (2015) A novel algorithm of lane detection addressing varied scenarios of curved and dashed lanemarks. In: 2015 International conference on image processing theory, tools and applications (IPTA), IEEE, pp. 87–92

  4. Kim Z (2008) Robust lane detection and tracking in challenging scenarios. IEEE Trans Intell Transp Syst 9(1):65–72

    Article  Google Scholar 

  5. McCall J, Trivedi MM (2004) An integrated, robust approach to lane marking detection and lane tracking. In: IEEE Intelligent Vehicles Symposium, 2004, IEEE, pp. 533–537

  6. Duong T-H, Chung S-T, Cho S (2014) Model-based robust lane detection for driver assistance. J Korea Multimed Soc 17(6):655–670

    Article  Google Scholar 

  7. Quach CH, Tran VL, Nguyen DH, Nguyen VT, Pham MT, Phung MD (2018) Real-time lane marker detection using template matching with RGB-D camera. In: 2018 2nd International conference on recent advances in signal processing, telecommunications & computing (SigTelCom), IEEE

  8. Heo H, Han G-T (2013) A robust real-time lane detection for sloping roads. KIPS Trans Softw Data Eng 2(6):413–422

    Article  Google Scholar 

  9. Han S-J, Han Y-J, Hahn H-S (2010) Lane and curvature detection algorithm based on the curve template matching method using top view image. J Inst Electron Eng Korea SP 47(6):97–106

    Google Scholar 

  10. Li J, Mei X, Prokhorov D, Tao D (2016) Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans Neural Netw Learn Syst 28(3):690–703

    Article  Google Scholar 

  11. Han J, Choi D, Park S, Hong S (2020) Hyperparameter optimization using a genetic algorithm considering verification time in a convolutional neural network. J Electr Eng Technol 15(2):721–726

    Article  Google Scholar 

  12. Khan MA, Choo J, Kim Y (2019) End-to-end partial discharge detection in power cables via time-domain convolutional neural networks. J Electr Eng Technol 14(3):1299–1309

    Article  Google Scholar 

  13. Neven D, De Brabandere B, Georgoulis S, Proesmans M, Gool LV (2018) Towards end-to-end lane detection: an instance segmentation approach. In: 2018 IEEE Intelligent vehicles symposium (IV), IEEE, pp. 286–291

  14. Zou Q, Jiang H, Yue Y, Chen L, Wang Q (2019) Robust lane detection from continuous driving scenes using deep neural networks. IEEE Trans Veh Technol 69(1):41–54

    Article  Google Scholar 

  15. Van Gansbeke W, De Brabandere B, Neven D, Proesmans M, Van Gool L (2019) End-to-end lane detection through differentiable least-squares fitting. In: Proceedings of the IEEE international conference on computer vision workshops

  16. Liu R, Yuan Z, Liu T Xiong Z (2021) End-to-end lane shape prediction with transformers. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 3694–3702

  17. Liu L, Chen X, Zhu S, Tan P (2021) CondLaneNet: a top-to-down lane detection framework based on conditional convolution," arXiv preprint arXiv:2105.05003

  18. Al-Jarrah OY, Yoo PD, Muhaidat S, Karagiannidis GK (2015) Efficient machine learning for big data: a review. Big Data Res 2(3):87–93

    Article  Google Scholar 

  19. Fatma S, Verma B, Asafuddoula M (2016) Impact of automatic feature extraction in deep learning architecture. In: 2016 International conference on digital image computing: techniques and applications (DICTA), IEEE, pp. 1–8

  20. Ruder S (2017) An overview of multi-task learning in deep neural networks," arXiv preprint arXiv:1706.05098

  21. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556

  22. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861

  23. Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6848–6856

  24. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp. 234–241

  25. Fritsch J, Kuehnl T, Geiger A (2013) A new performance measure and evaluation benchmark for road detection algorithms. In: 16th International IEEE conference on intelligent transportation systems (ITSC 2013), IEEE, pp. 1693–1700

  26. Longadge R, Dongre S (2013) Class imbalance problem in data mining review. arXiv preprint arXiv:1305.1707

  27. Schaffalitzky F, Zisserman A (2000) Planar grouping for automatic detection of vanishing lines and points. Image Vis Comput 18(9):647–658

    Article  Google Scholar 

  28. De Brabandere B, Neven D, Van Gool L (2017) Semantic instance segmentation with a discriminative loss function. arXiv preprint arXiv:1708.02551

  29. Pizzati F, Allodi M, Barrera A, Garcia F (2019) Lane detection and classification using cascaded CNNs. arXiv preprint arXiv:1907.01294

  30. Tabelini L, Berriel R, Paixao TM, Badue C, de Souza AF, Oliveira-Santos T (2021) Keep your eyes on the lane: real-time attention-guided lane detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 294–302

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Acknowledgements

This research was supported by the Ministry of Trade, Industry, and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the Project of Global Human Resources Cultivation for Innovative Growth (Project No.: P0008751).

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Authors and Affiliations

  1. Graduate School of Automotive Engineering, Kookmin University, Seoul, Korea

    Sun-Woo Baek & Myeong-Jun Kim

  2. Moovita Pte. Ltd, Singapore, Singapore

    Upendra Suddamalla, Anthony Wong & Bang-Hyon Lee

  3. Department of Automotive Engineering, Kookmin University, Seoul, Korea

    Jung-Ha Kim

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  1. Sun-Woo Baek
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  2. Myeong-Jun Kim
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  3. Upendra Suddamalla
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  4. Anthony Wong
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  5. Bang-Hyon Lee
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  6. Jung-Ha Kim
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Correspondence to Sun-Woo Baek.

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Baek, SW., Kim, MJ., Suddamalla, U. et al. Real-Time Lane Detection Based on Deep Learning. J. Electr. Eng. Technol. 17, 655–664 (2022). https://doi.org/10.1007/s42835-021-00902-6

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