AIRPLANES DETECTION IN AERIAL IMAGES USING YOLO NEURAL NETWORK

Authors

  • Volodymyr Kharchenko National Aviation University
  • Iurii Chyrka National Aviation University

DOI:

https://doi.org/10.18372/2306-1472.76.13149

Keywords:

Convolutional neural network, object detection, real-time processing, unmanned aerial vehicle

Abstract

Purpose: The represented research results are aimed to benchmark performance of state-of-the-art methods of objects detection. There were tested two popular single-stage neural networks based on the “you only looks once” approach. Methods: convolutional neural network, logistic regression, probabilistic theory, stochastic gradient descent. Results: The considered artificial neural network architectures for objects detection has been trained and applied for the particular task of the airplanes detection in aerial images taken from unmanned aerial vehicles and satellites. Discussion: Presented results of experimental verification prove their high detection ability, location precision and real-time processing speed using modern graphics processing unit. The considered neural networks can be easily re-trained for detection of different classes of ground objects.

Author Biographies

Volodymyr Kharchenko, National Aviation University

Doctor of Engineering Sciences. Professor.

Vice-Rector on Scientific Work of the National Aviation University, Kyiv, Ukraine.

Winner of the State Prize of Ukraine in Science and Technology, Honorable Worker of Science and Technology of Ukraine.

Education: Kyiv Institute of Civil Aviation Engineers, Kyiv, Ukraine.

Research area: management of complex socio-technical systems, air navigation systems and automatic decision-making systems aimed at avoidance conflict situations, space information technology design, air navigation services in Ukraine provided by CNS/АТМ systems

Iurii Chyrka, National Aviation University

Candidate of Engineering Sciences. Senior researcher.

National Aviation University.

Education: National Aviation University, Kyiv, Ukraine (2011).

Research area: computer vision, machine learning, control systems, radar signals processing, acoustic holography, and applied statistics.

References

Ammour N., Alhichri H., Bazi Y., Benjdira B., Alajlan N. and Zuair M. (2017) Deep Learning Approach for Car Detection in UAV Imagery. Remote Sensing, No. 9(312), pp. 1–15. doi: 10.3390/rs9040312

Maria G., Baccaglini E., Brevi D., Gavelli M., Scopigno R. (2016) A drone-based image processing system for car detection in a smart transport infrastructure. Proc. 18th Mediterranean Electrotechnical Conf. (MELECON). Limassol, Cyprus. doi: 10.1109/MELCON.2016.7495454

C. Castiblanco, J. Rodriguez, I. Mondragon, C. Parra, and J. Colorado Air Drones for Explosive Landmines Detection. in ROBOT2013: First Iberian Robotics Conference (M. A. Armada, A. Sanfeliu, M. Ferre Eds.) Springer International Publishing, pp. 107–114. doi: 10.1007/978-3-319-03653-3_9

Kharchenko V., Shmelova T., Sikirda, Y. (2012) Pryynyattya rishen' operatorom aeronavihatsiynoyi systemy. Monohrafiya [DecisionMaking of Operator in Air Navigation System.Monograph]. Kirovograd, KFA of NAU Publ., 292 p. (In Ukrainian)

Xu Y., Yu G., Wang Y., Wu X., and Ma Y. (2017) Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN. Journal of Advanced Transportation, Vol. 2017. doi: 10.1155/2017/2823617

Lee J., Wang J., Crandall D., Sabanovic S. and Fox G. (2017) Real-Time Object Detection for Unmanned Aerial Vehicles based on Cloud-based Convolutional Neural Networks. Proc. IEEE International Conference on Robotic Computing (IRC). Taichung, Taiwan. doi: 10.1109/IRC.2017.77

Leira F. S., Johansen T. A., Fossen T. I. (2015) Automatic Detection, Classification and Tracking of Objects in the Ocean Surface from UAVs Using a Thermal Camera. Proc. IEEE Aerospace Conference. Big Sky, MT, USA. doi: 10.1109/AERO.2015.7119238

Lee J.-N., Kwak K.-C. (2014) A Trends Analysis of Image Processing in Unmanned Aerial Vehicle. International Journal of Computer and Information Engineering, No. 2(8), pp. 271–274. doi: 10.1999/1307-6892/9997348

Dhawad S. R., Itkarkar R. R. (2016) Car Detecting Method using high Resolution images. International Journal on Recent and Innovation Trends in Computing and Communication, No. 2(4), pp. 197–203.

LeCun Y., Jackel L., Bottou L., Brunot A., Cortes C., Denker J., Drucker H., Guyon I., Müller U., Säckinger E., Simard P., Vapnik V. (1995) Comparison of learning algorithms for handwritten digits recognition. Proc. 1995 Int. Conf. on Artificial Neural Networks. Paris, France, pp. 53-60.

Viola P. and Jones M. (2001) Rapid object detection using a boosted cascade of simple features. Proc. Computer Vision and Pattern Recognition (CVPR-2001). Kauai, HI, USA, pp. I-511 – I-518. doi: 10.1109/CVPR.2001.990517

Dalal N. and Triggs B. (2005) Histograms of oriented gradients for human detection. Proc. Computer Vision and Pattern Recognition (CVPR-2005). San Diego, CA, USA, Vol. 1, pp. 886–891. doi: 10.1109/CVPR.2005.177

Girshick R., Donahue J., Darrell T., and Malik J. (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. Proc. Computer Vision and Pattern Recognition (CVPR-2014). Columbus, OH, USA, pp. 580–587. doi: 10.1109/CVPR.2014.81

Girshick R. (2015). Fast R-CNN. Proc. Computer Vision (ICCV-2015). Santiago, Chile, pp. 1440–1448. doi: 10.1109/ICCV.2015.169.

Ren S., He K., Girshick R., Sun J. (2015) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 6(39), pp. 1137–1149. doi: 10.1109/TPAMI.2016.2577031

Lin T.-Y., Doll´ar P., Girshick R., He K., Hariharan B., and Belongie S. (2017) Feature pyramid networks for object detection. Proc. Computer Vision and Pattern Recognition (CVPR-2017). Honolulu, HI, USA, pp. 936–944. doi: 10.1109/CVPR.2017.106

He K., Gkioxari G., Doll´ar P., and Girshick R. (2017) Mask R-CNN. Proc. Computer Vision (ICCV-2017). Venice, Italy, pp. 2980–2988. doi: 10.1109/ICCV.2017.322

He K., Zhang X., Ren S., and Sun J. (2016) Deep residual learning for image recognition. Proc. Computer Vision and Pattern Recognition (CVPR-2016). Las Vegas, NV, USA, pp. 770–778. doi: 10.1109/CVPR.2016.90.

Liu W., Anguelov D., Erhan D., Szegedy C., and Reed S. (2016) SSD: Single shot multibox detector. Proc. European Conference on Computer vision Computer Vision and Pattern Recognition (ECCV-2016). Amsterdam, The Netherlands. doi: 10.1007/978-3-319-46448-0_2

Redmon J., Divvala S., Girshick R., and Farhadi A. (2016) You only look once: Unified, real-time object detection. Proc. Computer Vision and Pattern Recognition (CVPR-2016). Las Vegas, NV, USA, pp. 779–788. doi: 10.1109/CVPR.2016.91

Redmon J. and Farhadi A. YOLOv3: An Incremental Improvement. Available at: https://pjreddie.com/media/files/papers/YOLOv3.pdf (Accessed 06.04.18)

Lin T.-Y., Goyal P., Girshick R., He K., Doll´ar P. (2017) Focal Loss for Dense Object Detection. Proc. Computer Vision (ICCV-2017). Venice, Italy, pp. 2999–3007. doi: 10.1109/ICCV.2017.324

Darknet: Open source neural networks in c. Available at: https://github.com/AlexeyAB/darknet (Accessed 05.04.18)

YoloMark. Available at: https://github.com/AlexeyAB/Yolo_mark (Accessed 05.04.18)

Published

22-11-2018

How to Cite

Kharchenko, V., & Chyrka, I. (2018). AIRPLANES DETECTION IN AERIAL IMAGES USING YOLO NEURAL NETWORK. Proceedings of National Aviation University, 76(3), 8–15. https://doi.org/10.18372/2306-1472.76.13149

Issue

Section

AEROSPACE SYSTEMS FOR MONITORING AND CONTROL