Abstract
Vision assisted navigation is an active area of research to assist pilots during bad weather conditions. However, these systems are not completely accurate. We propose 3D models to synthesize accurate 2D representations of the airport and the runway. A synthesized image sequence obtained from a 3D model of a view would be effective in conveying 3-D characteristics of the vanishing point (intersection between the horizon line and runway axis) and the beginning of the runway to the pilot. This can help to improve the pilot’s visual perception of the surroundings under adverse weather conditions, leading to a safer landing. We propose a system to generate 2D images of runway captured during takeoff and landing to provide better tracking. We analyze the results by segmenting the runway and comparing it with actual data captured by the aircraft.
Supported in part by Sichuan Science and Technology Program under Grant No. 2019YJ0541, the Open Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing under Grant No. 2019QZJ03 and Natural Science Foundation of Sichuan University of Science and Engineering (SUSE) under Grant No. 2019RC09, 2020RC28.
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Liu, C., Cheng, I., Zhang, Y., Basu, A.: Enhancement of low visibility aerial images using histogram truncation and an explicit retinex representation for balancing contrast and color consistency. ISPRS J. Photogrammetry Remote Sens. 128, 16–26 (2017)
Christie, G., et al.: Training object detectors with synthetic data for autonomous uav sampling applications. In: 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 352–357. IEEE (2018)
Marin, J., Vazquez, D., Geronimo, D., Lopez, A.M.: Learning appearance in virtual scenarios for pedestrian detection. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 137–144, June 2010
Tsirikoglou, A., Kronander, J., Wrenninge, M., Unger, J.: Procedural modeling and physically based rendering for synthetic data generation in automotive applications. CoRR abs/1710.06270 (2017)
Bhandari, N.: Procedural synthetic data for self-driving cars using 3D graphics. Ph.D. thesis, Massachusetts Institute of Technology (2018)
Liu, C., Cheng, I., Basu, A.: Synthetic vision assisted real-time runway detection for infrared aerial images. In: Basu, A., Berretti, S. (eds.) ICSM 2018. LNCS, vol. 11010, pp. 274–281. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04375-9_23
Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. CoRR abs/1605.06457 (2016)
Shafaei, A., Little, J.J., Schmidt, M.: Play and learn: Using video games to train computer vision models. CoRR abs/1608.01745 (2016)
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.: The SYNTHIA Dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: CVPR (2016)
Sun, B., Saenko, K.: From virtual to reality: fast adaptation of virtual object detectors to real domains. In: Proceedings of the British Machine Vision Conference 2014 (2014)
Jiang, C., et al.: Configurable, photorealistic image rendering and ground truth synthesis by sampling stochastic grammars representing indoor scenes. CoRR abs/1704.00112 (2017)
Pajares, G., De La Cruz, J.M.: A wavelet-based image fusion tutorial. Pattern Recognit. 37(9), 1855–1872 (2004)
Naidu, V., Raol, J.R.: Pixel-level image fusion using wavelets and principal component analysis. Defence Sci. J. 58(3), 338–352 (2008)
Li, H., Manjunath, B., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graph. Models Image Process. 57(3), 235–245 (1995)
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Sharma, H., Liu, C., Cheng, I. (2020). Synthetic Aerial Image Generation and Runway Segmentation. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_36
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DOI: https://doi.org/10.1007/978-3-030-54407-2_36
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