Abstract
With the development of deep learning, lane detection models based on deep convolutional neural networks have been widely used in autonomous driving systems and advanced driver assistance systems. However, in the case of harsh and complex environment, the performances of detection models degrade greatly due to the difficulty in merging long-range lane points with global context and exclusion of important higher-order information. To address these issues, we propose a new learning model to better capture lane features, called Deformable Transformer with high-order Deep Infomax (DTHDI) model. Specifically, we propose a Deformable Transformer neural network model based on segmentation techniques for high-accuracy detection, in which local and global contextual information is seamlessly fused and more information about the diversity of lane line shape features is retained, resulting in extraction of rich lane features. Meanwhile, we introduce a mutual information maximization approach for mining higher-order correlations among global shape, local shape, and lane position of lane lines to learn more discriminative representations of lane lines. In addition, we employ a row classification approach to further reduce the computational complexity for robust lane line detection. Our model is evaluated on two popular lane detection datasets. The empirical results show that the proposed DTHDI model outperforms the state-of-the-art methods.
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References
Wu, P., Chang, C., Lin, C.: Lane-mark extraction for automobiles under complex conditions. Pattern Recognit. 47, 2756–2767 (2014)
Hillel, A., Lerner, R., Levi, D., et al.: Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 25, 727–745 (2014)
Lin, T., Doll, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117–2125 (2017)
Pan, X., Shi, J., Luo, P., et al.: Spatial as deep: spatial cnn for traffific scene understanding. In: Proceeding of the 32nd AAAI conference on artificial intelligence, pp. 7276–7283 (2018)
Qin, Z., Wang, H., Li, X., et al.: Ultra-fast structure aware deep lane detection. In: Proceedings of European conference on computer vision, pp. 276–291 (2020)
Niu, J., Lu, J., Xu, M., et al.: Robust lane detection using two-stage feature extraction with curvefitting. Pattern Recognit. 59, 225–233 (2016)
Narote, S., Bhujbal, P., Narote, A., et al.: A review of recent advances in lane detection and departure warning system. Pattern Recognit. 73, 216–234 (2018)
Lee, M. Lee, J., Lee, D., et al.: Robust lane detection via expanded self-attention. (2021), arXiv:2102.07037
Xu, H., Wang, S., Cai, X., et al.: Curve lane-NAS: Unifying lane-sensitive architecture search and adaptive point blending. (2020), arXiv:2007.12147
Liu, R., Yuan, Z., Liu, T., et al.: End-to-end lane shape prediction with transformers. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 3694–3702 (2021)
Neven, D., Brabandere, B., Georgoulis, S., et al.: Towards end-to-end lane detection: an instance segmentation approach. In: IEEE intelligent vehicles symposium, pp. 286–291 (2018)
Zhang, J., Deng, T., Yan, F., et al.: Lane detection model based on spatio-temporal network with double convolutional gated recurrent units. IEEE Trans. Intell. Transp. Syst. 23(7), 6666–6678 (2021)
Su, J., Chen, C., Zhang, K., et al.: Structure guided lane detection. (2021), arXiv:2105.05403
Xu, H., Wang, S., Cai, X., et al.: Curve lane-NAs: Unifying lane-sensitive architecture search and adaptive point blending. In: Proceedings of the european conference on computer vision, pp. 689–704 (2020)
Lee, M., Lee, J., Lee, D., et al.: Robust lane detection via expanded self-attention. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 533–542 (2022)
Jayasinghe, O., Anhettigama, D., Hemachandra, S., et al.: Swiftlane: Towards fast and efficient lane detection. (2021), arXiv:2110.11779
Yoo, S., Lee, H., Myeong, H., et al.: End-to-end lane marker detection via row-wise classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 1006–1007 (2020)
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Annual conference on neural information processing systems, pp. 5998–6008 (2017)
Wang, W., Xie, E., Li, X., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. (2021), arXiv:2102.12122
Hjelm, R., Fedorov, A., Lavoie-Marchildon, S., et al.: Learning deep representations by mutual information estimation and maximization. (2019), arXiv:1808.06670
Mukherjee, S., Asnani, H., Kannan, S.: CCMI: Classifier based conditional mutual information estimation. In: Proceedings of the 35th uncertainty in artificial intelligence conference, pp. 1083–1093 (2020)
Bachman, P., Hjelm, R., Buchwalter, W.: Learning representations by maximizing mutual information across views. In: Proceedings of the 33rd international conference on neural information processing systems, pp. 15535–15545 (2019)
Xu, J., Vedaldi, A., Henriques, J.: Invariant information clustering for unsupervised image classification and segmentation. In: 2019 international conference on computer vision 1, pp. 9865–9874 (2019)
Chen, T., Kornblith, S., Norouzi, M., et al. A simple framework for contrastive learning of visual representations. (2020), arXiv:2002.05709
Tusimple, Tusimple lane detection benchmark (2017). https://github.com/TuSimple/tusimple-benchmark
Tusimple, Tusimple benchmark (2019). https://github.com/TuSimple/tusimple-benchmark
Philion, J.: Fastdraw: Addressing the long tail of lane detection by adapting a sequential prediction network. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11582–11591 (2019)
Hou, Y., Ma, Z., Liu, C., et al.: Learning lightweight lane detection cnns by self-attention distillation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 1013–1021 (2019)
Funding
This work described in this paper was supported by the Open Foundation of State Key Laboratory for Novel Software Technology at Nanjing University of P. R. China (No. KFKT2021B12). This work was supported in part by the Future Network Scientific Research Fund Project (FNSRFP-2021-YB-54), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17KJB520028), Tongda College of Nanjing University of Posts and Telecommunications (XK203XZ21001), Major Science and Technology Project of Jilin Province, China (20210301030GX), and Key Research and Development Program of Hubei Province, China (2021BAA179 and 2022BAA079). The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.
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RG: conceptualization, methodology, software. SH: data curation, writing-original draft preparation. LY: supervision, writing. LZ: supervision, writing - review and editing. HR: review, editing. YY: supervision, writing - review & editing. ZY: review, editing.
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Gao, R., Hu, S., Yan, L. et al. High-order deep infomax-guided deformable transformer network for efficient lane detection. SIViP 17, 3045–3052 (2023). https://doi.org/10.1007/s11760-023-02525-y
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DOI: https://doi.org/10.1007/s11760-023-02525-y