Artificial Neural Networks for Airport Runway Safety Systems
- Airport runway safety,
- artificial neural networks,
- video tracking,
- object detection,
- object classification
This paper presents the analysis of the existing approaches to ensuring the safety of aircraft`s takeoff and landing at airport runways using video surveillance systems. The subject area is formalized, and security threats and measures to prevent them are assessed. Optional architecture of the system designed for detection and classification of moving objects in the airport runway area is presented. The architecture is based on Neural Networks with AI elements. Also the original method of runway objects’ trajectory tracking is proposed. And finally, the research results of the applicability of the proposed architecture are presented.
Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). Simple online and realtime tracking. In Proceedings of the International Conference on Image Processing (ICIP). Phoenix, AZ, USA.
Girshick, R. B., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Computer Vision and Pattern, 2014. CVPR 2014. IEEE Conference.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
ICAO. (2006). Aerodrome design manual. Part 1 Runways (Doc 9157). ICAO.
ICAO. (2017). Runway Safety Programme - Global Runway Safety Action Plan. ICAO.
Itamar, T., Mechrez, R., & Zelnik-Manor, L. (2017). Template Matching with Deformable Diversity Similarity. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), (pp. 1311-1319).
Kalal, Z., Mikolajczyk, K., & Matas, J. (2010). Tracking-Learning-Detection. IEEE transactions on pattern analysis and machine intelligence, 6(1), pp. 1-14.
Matthews, P. (1995). Airports. In P. Matthews, The Guinness Book of Records (p. 128). Guinness Superlatives.
Ning, G., Zhang, Z., Huang, C., He, Z., Ren, X., & Wang, H. (2017). Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking. 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (pp. 1-4). Baltimore, MD.
Oppenheim, A. V., & Schafer, R. W. (1975). Digital Signal Processing. Prentice Hall.
Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv:1804.02767. Washington: University of Washington.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., . . . Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 1-9). Boston, MA.
Zhao, Z., Zheng, P., Xu, S., & Wu, X. (2019, November). Object Detection with Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212-3232.