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Lane Detection Based on DeepLab

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Proceedings of the 9th International Conference on Computer Engineering and Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1143))

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Abstract

Lane detection is an important part of car autopilot. It helps the vehicle to stabilize itself in the lane, avoid risks, and determine the direction of driving. In this paper, we propose a neural network approach to detect lanes in different conditions. We also collect 1761 frames of front-view pictures from driving recorders, preprocess them with ROI analysis as training and testing data. Resulted models have overall high accuracy over tests.

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References

  1. Taylor, C., Seck, J., Blasi, R., Malik, J.: A comparative study of vision-based lateral control strategies for autonomous highway driving. Int. J. Robot. Res. (1999)

    Google Scholar 

  2. Mccall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans. Intell. Transp. Syst. 7(1), 20–37 (2006)

    Article  Google Scholar 

  3. Bertozzi, M., Broggi, A.: Real-time lane and obstacle detection on the gold system. In: Intelligent Vehicles Symposium, pp. 213–218 (1996)

    Google Scholar 

  4. Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  5. Krizhevsky, A., Sutskever, I., Hinton, G.E., et al.: Imagenet classification with deep convolutional neural networks. Neural Inf. Process. Syst. 141(5), 1097–1105 (2012)

    Google Scholar 

  6. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  7. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  8. Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM Multimedia, pp. 675–678 (2014)

    Google Scholar 

  9. Long, J., Shelhamer, E., Darrell, T., et al.: Fully convolutional networks for semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  10. Chen, L., Papandreou, G., Kokkinos, I., et al.: Deep lab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    Article  Google Scholar 

  11. Chen, L., Papandreou, G., Kokkinos, I., et al.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: International Conference on Learning Representations (2015)

    Google Scholar 

  12. Azimi, S.M., Fischer, P., Korner, M., et al.: Aerial LaneNet: lane-marking semantic segmentation in aerial imagery using wavelet-enhanced cost-sensitive symmetric fully convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 57(5), 2920–2938 (2019)

    Article  Google Scholar 

  13. Pan, X., Shi, J., Luo, P., et al.: Spatial as deep: spatial CNN for traffic scene understanding. In: Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  14. Philion, J.: FastDraw: addressing the long tail of lane detection by adapting a sequential prediction network. In: Computer Vision and Pattern Recognition, pp. 11582–11591 (2019)

    Google Scholar 

  15. Aly, M.: Real time detection of lane markers in urban streets. In: IEEE Intelligent Vehicles Symposium, pp. 7–12 (2008)

    Google Scholar 

  16. Wu, P., Chang, C., Lin, C.H., et al.: Lane-mark extraction for automobiles under complex conditions. Pattern Recognit. 47(8), 2756–2767 (2014)

    Article  Google Scholar 

  17. Narote, S.P., Bhujbal, P.N., Narote, A.S., et al.: A review of recent advances in lane detection and departure warning system. Pattern Recognit. 216–234 (2018)

    Google Scholar 

  18. Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)

    Article  Google Scholar 

  19. Zheng, F., Luo, S., Song, K., et al.: Improved lane line detection algorithm based on hough transform. Pattern Recognit. Image Anal. 28(2), 254–260 (2018)

    Article  Google Scholar 

  20. Zou, Q., Jiang, H., Dai, Q., et al.: Robust lane detection from continuous driving scenes using deep neural networks. In: Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  21. Lafferty, J.D., Mccallum, A., Pereira, F., et al.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning, pp. 282–289 (2001)

    Google Scholar 

  22. https://en.desaysv.com/

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Author information

Authors and Affiliations

  1. University of Southern California, Los Angeles, USA

    Mingzhe Li

Authors
  1. Mingzhe Li
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Corresponding author

Correspondence to Mingzhe Li .

Editor information

Editors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Qi Liu

  2. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Xiaodong Liu

  3. College of Computer Science and Technology, Hengyang Normal University, Hengyang, Hunan, China

    Lang Li

  4. Department of Informatics, University of Leicester, Leicester, UK

    Huiyu Zhou

  5. College of Computer Science and Technology, Hengyang Normal University, Hengyang, Hunan, China

    Hui-Huang Zhao

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Li, M. (2021). Lane Detection Based on DeepLab. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_47

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