High dynamic range imaging; Neural network; Road lane detection; Communication; Media Technology; Industrial and Manufacturing Engineering
Résumé :
[en] There are several studies on road lane detection but very few address adverse conditions for acquisition such as sun glare. Loss of details in underexposed images captured facing a low sun leads to misleading road lane detection. High Dynamic Range Imaging methods are used to acquire most details in such scenes. Unfortunately, these techniques are heavy on computations and therefore unsuitable for real time road lane detection. In this paper, we propose a machine learning solution that avoids High Dynamic Range Imaging computations that are the radiance map estimation, tone-mapping algorithms and quality measures calculation. We train a neural network on a High Dynamic Range Imaging dataset. The resulting model produces suitable images for road lane detection facing sun glare, in real time. Subjective and objective comparisons with the most popular High Dynamic Range Imaging method, Mertens Algorithm, are conducted to prove the effectiveness of the proposed Neural Network. The delivered images demonstrated an improvement in road lane detection.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
El Amine Moumene, Mohammed; Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria
Benkedadra, Mohamed ; Université de Mons - UMONS ; Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria
S. Yenikaya, G. Yenikaya, and E. Duven, “Keeping the vehicle on the road a survey on on-road lane detection systems,” acm Computing Surveys (CSUR), vol. 46, October 2013.
P. Wu, C. Chang, and C. H. Lin, “Lane-mark extraction for automobiles under complex conditions,” Pattern Recognition, vol. 47, p. 27562767, August 2014.
B. Yu and A. Jain, “Lane boundary detection using a multiresolution hough transform,” International Conference on Image Processing, vol. 2, 1997, pp. 748-751.
A. B. Hillel, R. Lerner, D. Levi, and G. Raz, “Recent progress in road and lane detection: a survey,” Machine Vision and Applications, vol. 25, pp. 727-745, April 2014.
C. Yuan, H. Chen, J. Liu, D. Zhu and Y. Xu, “Robust Lane Detection for Complicated Road Environment Based on Normal Map,” in ieee Access, vol. 6, pp. 49679-49689, 2018.
J. A. Christopher Rose, Jordan Britt and D. Bevly, “An integrated vehicle navigation system utilizing lane-detection and lateral position estimation systems in difficult environments for GPS,” ieee transactions on intelligent transport systems, vol. 15, pp. 2615-2628, 2014.
P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in ACM siggraph 2008 classes, pp. 1-10, 2008.
C. Aguerrebere, J. Delon, Y. Gousseau, and P. Musé, “Best algorithms for HDR image generation. a study of performance bounds,” SIAM Journal on Imaging Sciences, vol. 7, no. 1, pp. 1-34, 2014.
Y. Salih, W. bt. Md-Esa, A. S. Malik, and N. Saad, “Tone mapping of HDR images: A review,” 4th International Conference on Intelligent and Advanced Systems (ICIAS2012), vol. 1, pp. 368-373, June 2012.
T. Mertens, J. Kautz and F. Van Reeth, “Exposure Fusion,” 15th Pacific Conference on Computer Graphics and Applications (PG'07), Maui, HI, 2007, pp. 382-390.
F. Kou, W. Chen, J. Wang and Z. Zhao, “A lane boundary detection method based on high dynamic range image,” IEEE 10th International Conference on Industrial Informatics, Beijing, 2012, pp. 21-25.
M. E. Moumene, R. Nourine and D. Ziou, “Generalized Exposure Fusion Weights Estimation,” 2014 Canadian Conference on Computer and Robot Vision, Montreal, QC, 2014, pp. 71-76.
M. E. Moumene, R. Nourine and D. Ziou, “Fast Exposure Fusion Based on Histograms Segmentation,” International Conference on Image and Signal Processing, France, 2014, pp. 367-374.
M. D. Fairchild, “The HDR photographic survey dataset (HDRPS),” 2017: http://markfairchild.org/HDR.html.
Diederik P. Kingma, Jimmy Ba, “Adam: A Method for Stochastic Optimization”, 3rd International Conference on Learning Representations, 2015.
M.E.Moumene, M. Benkedadra, “Exposure fusion Neural Network”, 2021: https://efnn.ml.
Guihong, Dali and Pingfan, Y., “Information measure for performance of image fusion”, Electronics Letters 28(7):313-315. 2002.
Amol Borkar, Monson Hayes, Matk T. Smith, “A novel lane detection system with efficient ground truth generation,” IEEE transactions on intelligent transport systems, vol. 13, pp. 365-374, 2012.
A. Lopez, J. Serrat, C. Canero, F. Lumbreras, and T. Graf, “Robust lane markings detection and road geometry computation,” International Journal of Automotive Technology, vol. 11, pp. 395-407, 2010.
