Abstract
Lane detection is a challenging task in the field of vision detection. The annotation information of lane is very sparse, and it is faced with the interference of occlusion, illumination and other factors, which seriously affects the capture of lane features by neural network. In this paper, we propose the Self-Attention Lane Segmentation Network (SALSN) which allows attention-driven, long-range dependency modeling for lane detection task. Although traditional convolutional neural networks have demonstrated their powerful performance, their ability to capture global relationships in images has not been fully explored. We introduce a self-attentive module to model the long-range dependencies between lane features. Lanes have strong shape constraints but weak coherence. In SALSN, we utilize a dense feature fusion framework to better capture lane context information and use all element information to generate lane segmentation images. Experimental results show that SALSN is not only effective in learning the remote dependencies of lane features, but also significantly improves the lane detection performance. We have validated our approach on two large-scale lane detection datasets, and our method can achieve more competitive results.
This research was supported in part by National Key Research and Development Plan Key Special Projects under Grant No. 2018YFB2100303, Shandong Province colleges and universities youth innovation technology plan innovation team project under Grant No. 2020KJN011, Shandong Provincial Natural Science Foundation under Grant No. ZR2020MF060, Program for Innovative Postdoctoral Talents in Shandong Province under Grant No. 40618030001, National Natural Science Foundation of China under Grant No. 61802216, and Postdoctoral Science Foundation of China under Grant No. 2018M642613.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Peng, T., Su, L., Zhang, R., Guan, Z., Zhao, H., Qiu, Z.: A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles. Expert Syst. Appl. 141, 112953 (2020)
Tang, J., Li, S., Liu, P.: A review of lane detection methods based on deep learning. Pattern Recogn. 111, 107623 (2021)
Wu, P.-C., Chang, C.-Y., Lin, C.H.: Lane-mark extraction for automobiles under complex conditions. Pattern Recogn. 47(8), 2756–2767 (2014)
Fan, R., Wang, H., Cai, P., Liu, M.: SNE-RoadSeg: incorporating surface normal information into semantic segmentation for accurate freespace detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 340–356. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_21
Ko, Y., Lee, Y., Azam, S., Munir, F., Jeon, M., Pedrycz, W.: Key points estimation and point instance segmentation approach for lane detection. IEEE Trans. Intell. Transp. Syst. 23(7), 8949–8958 (2022)
Lv, Z., Li, J., Dong, C., Li, H., Xu, Z.: Deep learning in the COVID-19 epidemic: a deep model for urban traffic revitalization index. Data Knowl. Eng. 135, 101912 (2021)
Lv, Z., Li, J., Dong, C., Xu, Z.: DeepSTF: a deep spatial-temporal forecast model of taxi flow. Comput. J. (2021)
Zheng, T., et al.: RESA: recurrent feature-shift aggregator for lane detection. In: AAAI Conference on Artificial Intelligence, pp. 3547–3554 (2021)
Liu, L., Chen, X., Zhu, S., Tan, P.: CondLaneNet: a top-to-down lane detection framework based on conditional convolution. In: IEEE/CVF International Conference on Computer Vision, pp. 3773–3782 (2021)
Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., Oliveira-Santos, T.: Keep your eyes on the lane: real-time attention-guided lane detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 294–302 (2021)
Lee, M., Lee, J., Lee, D., Kim, W., Hwang, S., Lee, S.: Robust lane detection via expanded self attention. In: Winter Conference on Applications of Computer Vision, pp. 533–542 (2022)
Zhang, J., Lu, C., Li, X., Kim, H.-J., Wang, J.: A full convolutional network based on DenseNet for remote sensing scene classification. Math. Biosci. Eng. 16(5), 3345–3367 (2019)
Wu, Z., Shen, C., Van Den Hengel, A.: Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recogn. 90, 119–133 (2019)
TuSimple: Tusimple benchmark. https://github.com/TuSimple/tusimple-benchmark. Accessed Nov 2019
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection CNNs by self attention distillation. In: IEEE International Conference on Computer Vision, pp. 1013–1021 (2019)
Su, J., Chen, C., Zhang, K., Luo, J., Wei, X., Wei, X.: Structure guided lane detection. arXiv preprint arXiv:2105.05403 (2021)
DataLake. https://aistudio.baidu.com/aistudio/datasetdetail/54289?_=1621952478922
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, G., Li, J., Wang, Y., Meng, X. (2022). Incorporating Self Attention Mechanism into Semantic Segmentation for Lane Detection. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-19214-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19213-5
Online ISBN: 978-3-031-19214-2
eBook Packages: Computer ScienceComputer Science (R0)