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3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection

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Artificial Neural Networks and Machine Learning – ICANN 2022 (ICANN 2022)

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Abstract

Lane detection is one of the most fundamental tasks for autonomous driving. It plays a crucial role in the lateral control and the precise localization of autonomous vehicles. Monocular 3D lane detection methods provide state-of-the-art results for estimating the position of lanes in 3D world coordinates using only the information obtained from the front-view camera. Recent advances in Neural Architecture Search (NAS) facilitate automated optimization of various computer vision tasks. NAS can automatically optimize monocular 3D lane detection methods to enhance the extraction and combination of visual features, consequently reducing computation loads and increasing accuracy. This paper proposes 3DLaneNAS, a multi-objective method that enhances the accuracy of monocular 3D lane detection for both short- and long-distance scenarios while at the same time providing a fair amount of hardware acceleration. 3DLaneNAS utilizes a new multi-objective energy function to optimize the architecture of feature extraction and feature fusion modules simultaneously. Moreover, a transfer learning mechanism is used to improve the convergence of the search process. Experimental results reveal that 3DLaneNAS yields a minimum of 5.2% higher accuracy and \(\approx \)1.33\(\times \) lower latency over competing methods on the synthetic-3D-lanes dataset. Code is at https://github.com/alizoljodi/3DLaneNAS

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References

  1. Amine, K.: Multiobjective simulated annealing: principles and algorithm variants. Adv. Oper. Res. 2019 (2019)

    Google Scholar 

  2. Bai, M., Mattyus, G., Homayounfar, N., Wang, S., Lakshmikanth, S.K., Urtasun, R.: Deep multi-sensor lane detection. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3102–3109. IEEE (2018)

    Google Scholar 

  3. Borji, A.: Vanishing point detection with convolutional neural networks. arXiv preprint arXiv:1609.00967 (2016)

  4. Cai, H., Gan, C., Wang, T., Zhang, Z., Han, S.: Once-for-all: train one network and specialize it for efficient deployment. arXiv preprint arXiv:1908.09791 (2019)

  5. Chen, P.Y., Lee, C.M., Yeh, H.Z., Huang, Y.C.: Design and implementation for a vision-guided wheeled mobile robot system. In: 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1–5 (2018). https://doi.org/10.1109/ICCE-China.2018.8448543

  6. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009). https://doi.org/10.1109/CVPR.2009.5206848

  7. Dong, X., Liu, L., Musial, K., Gabrys, B.: Nats-bench: benchmarking nas algorithms for architecture topology and size. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3634–3646 (2021). https://doi.org/10.1109/TPAMI.2021.3054824

    Article  Google Scholar 

  8. Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(1), 1997–2017 (2019)

    MathSciNet  MATH  Google Scholar 

  9. Foundation, B.: Home of the blender project - free and open 3D creation software. https://www.blender.org/

  10. Garnett, N., Cohen, R., Pe’er, T., Lahav, R., Levi, D.: 3d-lanenet: end-to-end 3d multiple lane detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2921–2930 (2019)

    Google Scholar 

  11. Guo, Y., et al.: Gen-lanenet: a generalized and scalable approach for 3D lane detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 666–681. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58589-1_40

    Chapter  Google Scholar 

  12. Gurghian, A., Koduri, T., Bailur, S.V., Carey, K.J., Murali, V.N.: Deeplanes: end-to-end lane position estimation using deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38–45 (2016). https://doi.org/10.1109/CVPRW.2016.12

  13. He, X., Zhao, K., Chu, X.: Automl: a survey of the state-of-the-art. Knowl.-Based Syst. 212, 106622 (2021)

    Google Scholar 

  14. Hsu, C.H., et al.: Monas: multi-objective neural architecture search using reinforcement learning. arXiv preprint arXiv:1806.10332 (2018)

  15. Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems 28 (2015)

    Google Scholar 

  16. Jung, J., Bae, S.H.: Real-time road lane detection in urban areas using lidar data. Electronics 7(11), 276 (2018)

    Article  Google Scholar 

  17. Kheyrollahi, A., Breckon, T.P.: Automatic real-time road marking recognition using a feature driven approach. Mach. Vis. Appl. 23(1), 123–133 (2012). https://doi.org/10.1007/s00138-010-0289-5

    Article  Google Scholar 

  18. Lee, S., et al.: Vpgnet: vanishing point guided network for lane and road marking detection and recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1947–1955 (2017)

    Google Scholar 

  19. Lindauer, M., Hutter, F.: Best practices for scientific research on neural architecture search. J. Mach. Learn. Res. 21(243), 1–18 (2020)

    MATH  Google Scholar 

  20. Liu, C., et al.: Auto-deeplab: hierarchical neural architecture search for semantic image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 82–92 (2019)

    Google Scholar 

  21. Liu, H., Simonyan, K., Yang, Y.: Darts: differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)

  22. Loni, M., Mousavi, H., Riazati, M., Daneshtalab, M., Sjödin, M.: Tas: ternarized neural architecture search for resource-constrained edge devices. In: Design, Automation & Test in Europe Conference & Exhibition DATE 2022, 14 March 2022, Antwerp, Belgium. IEEE (2022). http://www.es.mdh.se/publications/6351-

  23. Loni, M., Sinaei, S., Zoljodi, A., Daneshtalab, M., Sjödin, M.: Deepmaker: a multi-objective optimization framework for deep neural networks in embedded systems. Microprocess. Microsyst. 73, 102989 (2020)

    Article  Google Scholar 

  24. Loni, M., et al.: Densedisp: resource-aware disparity map estimation by compressing siamese neural architecture. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2020). https://doi.org/10.1109/CEC48606.2020.9185611

  25. Loni, M., et al.: Faststereonet: a fast neural architecture search for improving the inference of disparity estimation on resource-limited platforms. IEEE Trans. Syst. Man Cybern. Syst. 52(8), 1–13 (2021). https://doi.org/10.1109/TSMC.2021.3123136

    Article  Google Scholar 

  26. Loni, M., Zoljodi, A., Sinaei, S., Daneshtalab, M., Sjödin, M.: NeuroPower: designing energy efficient convolutional neural network architecture for embedded systems. In: Tetko, I.V., Kürková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11727, pp. 208–222. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30487-4_17

    Chapter  Google Scholar 

  27. Mallot, H.A., Bülthoff, H.H., Little, J., Bohrer, S.: Inverse perspective mapping simplifies optical flow computation and obstacle detection. Biol. Cybern. 64(3), 177–185 (1991)

    Article  MATH  Google Scholar 

  28. Nedevschi, S., Oniga, F., Danescu, R., Graf, T., Schmidt, R.: Increased accuracy stereo approach for 3D lane detection. In: 2006 IEEE Intelligent Vehicles Symposium, pp. 42–49. IEEE (2006)

    Google Scholar 

  29. Nedevschi, S., et al.: 3D lane detection system based on stereovision. In: Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No. 04TH8749), pp. 161–166. IEEE (2004)

    Google Scholar 

  30. Padilla, R., Netto, S., da Silva, E.: A survey on performance metrics for object-detection algorithms (2020). https://doi.org/10.1109/IWSSIP48289.2020

  31. Pizzati, F., García, F.: Enhanced free space detection in multiple lanes based on single cnn with scene identification. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 2536–2541 (2019). https://doi.org/10.1109/IVS.2019.8814181

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

  33. Tang, J., Li, S., Liu, P.: A review of lane detection methods based on deep learning. Pattern Recogn. 111, 107623 (2021)

    Google Scholar 

  34. White, C., Nolen, S., Savani, Y.: Exploring the loss landscape in neural architecture search. In: Uncertainty in Artificial Intelligence, pp. 654–664. PMLR (2021)

    Google Scholar 

  35. Xu, H., Wang, S., Cai, X., Zhang, W., Liang, X., Li, Z.: CurveLane-NAS: unifying lane-sensitive architecture search and adaptive point blending. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 689–704. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58555-6_41

    Chapter  Google Scholar 

  36. Zhang, W., Mahale, T.: End to end video segmentation for driving: lane detection for autonomous car. arXiv preprint arXiv:1812.05914 (2018)

  37. Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)

  38. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018)

    Google Scholar 

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Acknowledgement

This work was supported by Sweden’s Innovation Agency (VINNOVA) within the AutoDeep project, and KKS within the DPAC project. This work has been also partially conducted in the project ICT programme which was supported by the European Union through the European Social Fund.

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Authors and Affiliations

  1. School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden

    Ali Zoljodi, Mohammad Loni, Sadegh Abadijou & Masoud Daneshtalab

  2. Zenseact AB, Gothenburg, Sweden

    Mina Alibeigi

  3. Department of Computer Systems, Tallinn University of Technology, Tallinn, Estonia

    Masoud Daneshtalab

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  1. Ali Zoljodi
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  2. Mohammad Loni
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  3. Sadegh Abadijou
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  4. Mina Alibeigi
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  5. Masoud Daneshtalab
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Corresponding author

Correspondence to Ali Zoljodi .

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Editors and Affiliations

  1. University of the West of England, Bristol, UK

    Elias Pimenidis

  2. Lancaster University, Lancaster, UK

    Plamen Angelov

  3. Digital Innovation, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  5. The University of the West of England, Bristol, UK

    Mehmet Aydin

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Zoljodi, A., Loni, M., Abadijou, S., Alibeigi, M., Daneshtalab, M. (2022). 3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_34

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  • DOI: https://doi.org/10.1007/978-3-031-15919-0_34

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