Abstract
Lane departure warning (LDW) system attached to modern vehicles is responsible for lowering car accident caused by inappropriate lane changing behaviour. However the success of LDW system depends on how well it define and segment the drivable ego lane. As the development of deep learning methods, the expensive light detection and ranging (LIDAR) guided system is now replaced by analysis of digital images captured by low-cost camera. Numerous method has been applied to address this problem. However, most approach only focusing on achieving segmentation accuracy, while in the real implementation of LDW, computational time is also an importance metric. This research focuses on utilizing deconvolutional neural network to generate accurate road lane segmentation in a realtime fashion. Feature maps from the input image is learned to form a representation. The use of convolution and pooling layer to build the feature map resulting in spatially smaller feature map. Deconvolution and unpooling layer then applied to the feature map to reconstruct it back to its input size. The method used in this research resulting a 98.38% pixel level accuracy and able to predict a single input frame in 28 ms, enabling realtime prediction which is essential for a LDW system.
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References
World Health Organization: Global status report on road safety: time for action (2009). http://apps.who.int/iris/bitstream/10665/44122/1/9789241563840_eng.pdf. Accessed 2 Sept 2016
IFRCRCS: Practical Guide on Road Safety: A Toolkit for Red Cross and Red Crescent Societies (2007). http://www.grsproadsafety.org/themes/default/pdfs/GRSPRed%20Cross%20Toolkit.pdf. Accessed 7 Sept 2016
Kusano, K.D., dan Gabler, H.C.: Comparison of expected crash and injury reduction from production forward collision and lane departure warning systems. Traffic Injury prevent. 16(sup2), S109–S114 (2015)
Jonathan, L., Shelhamer, E., dan Darrell, T.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Mohan, R.: Deep deconvolutional networks for scene parsing (2014). https://arxiv.org/pdf/1411.4101.pdf. 15 November 2014. Accessed 22 Oct (2016)
Brust, C.A., et al.: Efficient convolutional patch networks for scene understanding. In: CVPR Scene Understanding Workshop (2015)
Mendes, C.C.T., Fremont, V., dan Wolf, D.F.: Exploiting fully convolutional neural networks for fast road detection. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3174–3179 (2016)
Noh, H., Seunghoon H., dan Bohyung H.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision 2015, pp. 1520–1528 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Kingma, D., Jimmy B.: Adam: a method for stochastic optimization (2014). https://arxiv.org/pdf/1412.6980.pdf. 30 Januari 2017. Accessed 31 Mar 2017
Mendes, C., Frémont, V., Wolf, D.: Vision-Based Road Detection using Contextual Blocks (2015). https://arxiv.org/pdf/1412.6980.pdf. 30 Januari 2017. Accessed 10 Mar 2017
He, B., Ai, R., Yan, Y., dan Lang, X.: Accurate and robust lane detection based on dual-view convolutional neural network. In: Intelligent Vehicles Symposium (IV), pp. 1041–1046 (2016)
Laddha, A., Kocamaz, M.K., Navarro-Serment, L.E., dan Hebert, M.: Map-supervised road detection. In: Intelligent Vehicles Symposium (IV), pp. 118–123 (2016)
Niu, J., Lu, J., Xu, M., Lv, P., dan Zhao, X.: Robust lane detection using two-stage feature extraction with curve fitting. Pattern Recogn. 59, 225–233 (2015)
Aly, M.: Real time detection of lane markers in urban streets. In: Intelligent Vehicles Symposium, pp. 7–12 (2008)
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Nugroho, D.P.A., Riasetiawan, M. (2017). Road Lane Segmentation Using Deconvolutional Neural Network. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_2
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DOI: https://doi.org/10.1007/978-981-10-7242-0_2
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