Abstract
Lane detection is used to detect the lane markings in a road scene between which the vehicle is driving and provide the accurate location and shape of each lane marking. It serves as one of the key techniques to enable modern, assisted, and autonomous driving systems. However, lane detection poses several challenges. The lane markings vary in their shapes, colors, and patterns. The lack of distinct features and the presence of several occlusions on the roads makes the use of conventional methods using handcrafted features less robust and computationally expensive. In this study, we propose a compact and efficient multi-stage Convolutional Neural Network (CNN) architecture which can learn both the lane markings segmentation and also the localization and shape of each lane in the form of key-points. The proposed model combines a lane mask proposal network with a lane key-point determination network to accurately predict the key-points that describe the left and right lane-markings of the vehicle lanes. The high running speed and low computational cost of the proposed method make it suitable for being deployed in the real world vehicle systems. Through simulation results, we also show that the proposed method is robust to a variety of weather conditions and highway driving scenarios.
Similar content being viewed by others
References
Amaradi P, Sriramoju N, Li Dang GS, Tewolde, Kwon J (2016) Lane following and obstacle detection techniques in autonomous driving vehicles. IEEE International Conference on Electro Information Technology (EIT), Grand Forks, ND, pp 0674–0679
Ayachi R, Afif M, Yahia Said, and Atri M (2018) Strided convolution instead of max pooling for memory efficiency of convolutional neural networks. In: International conference on the Sciences of Electronics, Technologies of Information and Telecommunications, pp 234–243. Springer, Cham
Badrinarayanan V, Kendall A, Cipolla R, Segnet (2017) A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Bellis EA, Page J (2008) National Motor Vehicle Crash Causation Survey (NMVCCS) SAS Analytical Users Manual.
Chougule S, Ismail A, Soni A, Kozonek N, Narayan V, Schulze M (2018) An efficient encoder-decoder CNN architecture for reliable multilane detection in real time. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp 1444–1451. IEEE, Piscataway
Gayko JE (2012) Lane departure and lane keeping. Handbook of Intelligent Vehicles 1:689–708
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770-778
Hou Y, Ma Z, Liu C, Loy CC (2019) Learning lightweight lane detection cnns by self attention distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1013–1021
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
Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167
Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ayed IB, 2019, May. Boundary loss for highly unbalanced segmentation. In: International conference on medical imaging with deep learning, pp 285–296
Li Mingfa L Yuanyuan, J Min (2018) Lane detection based on connection of various feature extraction methods. Advances in multimedia. Hindawi, London, vol 24, pp 1687–5680
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Muthalagu R, Bolimera A, Kalaichelvi V (2020) Lane detection technique based on perspective transformation and histogram analysis for self-driving cars. Comput Electr Eng 85:106653
Neven D, Brabandere BD, Georgoulis S, Proesmans M, Luc Van Gool (2008) Towards end-to-end lane detection: an instance segmentation approach.. In: 2018 IEEE intelligent vehicles symposium (IV), pp 286–291. IEEE, Piscataway
Neven D, De Brabandere B, Georgoulis S, Proesmans M, Van Gool L (2018) Towards end-to-end lane detection: an instance segmentation approach. In: 2018 IEEE intelligent vehicles symposium (IV), pp 286–291. IEEE, Piscataway
Oliveira M, Santos V, Sappa AD (2015) Multimodal inverse perspective mapping. Inf Fusion 24:108–121
Paszke A, Chaurasia A, Kim S, Culurciello E (2016) Enet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241
Singh S (2015, February) Critical reasons for crashes investigated in the National Motor Vehicle Crash Causation Survey. (Traffic Safety Facts Crash•Stats. Report No. DOT HS 812 115). National Highway Traffic Safety Administration, Washington, DC
Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 39, no 4, pp 640–651. https://doi.org/10.1109/TPAMI.2016.2572683
Tabelini L, Berriel R, Paixão TM, Badue C, Alberto F De Souza, Oliveira-Santos T (2020) PolyLaneNet: Lane estimation via deep polynomial regression. arXiv preprint arXiv:2004.10924
Talib ML, Rui X, Ghazali KH, Mohd. Zainudin N, Ramli S (2013) Comparison of edge detection technique for lane analysis by improved hough transform, vol 8237. Springer, Cham, pp 176–183
The tuSimple lane challenge. http://benchmark.tusimple.ai/
Visvikis C et al (2008) Study on lane departure warning and lane change assistant systems.. Transport Research Laboratory Project Rpt PPR, 374
Wu H, Zhang J, Huang K, Liang K, Yizhou Yu (2019) Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation. arXiv preprint arXiv:1903.11816
Xing Y et al (2018) Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision. IEEE/CAA J Automat Sin 5(3):645–661. https://doi.org/10.1109/JAS.2018.7511063
Pan X, Shi J, Luo P, Wang X, Tang X (2018) Spatial as deep: Spatial CNN for traffic scene understanding. AAAI
Yan X, Li Y (2017) A method of lane edge detection based on Canny algorithm. 2017 Chinese Automation Congress (CAC), pp 2120–2124
Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H (2015) Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Muthalagu, R., Bolimera, A. & Kalaichelvi, V. Vehicle lane markings segmentation and keypoint determination using deep convolutional neural networks. Multimed Tools Appl 80, 11201–11215 (2021). https://doi.org/10.1007/s11042-020-10248-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10248-2