TECHNOLOGY FOR INDOOR DRONE POSITIONING BASED ON CNN DETECTOR
Abstract and keywords
Abstract (English):
The article presents the drone positioning technology in a multi-camera system by using the detection algorithm. Paper describes positioning system and algorithm for calculating 3d drone coordinates based on its image position, detected on images of stationary video cameras. Positioning enables automatically control the drone when precise data from satellite navigation systems are not available, for example, in closed hangars. The developed technology is used to create a complex of automatic visual control of aircraft. The ways of adaptation of neural network detection algorithm to the problem of drone detection are presented. The main attention is paid to the methods of training data preparation. It is shown that high accuracy can be achieved using synthesized images without any real data or manual labelling.

Keywords:
object detection, neural networks, drones, positioning, indoor navigation, multi-camera system, image synthesis
References

1. Yu.B. Blokhinov, V.A. Gorbachev, A.D. Nikitin, S.V.Skryabin. Technology for Visual Inspection of AircraftSurfaces using Programmable Unmanned Aerial Vehicles.Journal of Computer and Systems Sciences International.Received by the editor 28.06.2019.

2. R. Girshick, J. Donahue, T. Darrell, J. Malik. Rich featurehierarchies for accurate object detection and semanticsegmentation. In Proceedings of the IEEE conference oncomputer vision and pattern recognition, p. 580-587, 2014.

3. J. R. Uijlings, K. E. Van De Sande, T. Gevers, A. W.Smeulders. Selective search for object recognition.International journal of computer vision, 104(2):154-171,2013.

4. R. Girshick. Fast r-cnn. In Proceedings of the IEEEinternational conference on computer vision, p. 1440-1448, 2015.

5. S. Ren, K. He, R. Girshick, J. Sun. Faster R-CNN:Towards real-time object detection with region proposalnetworks. In Advances in neural information processingsystems, p. 91-99, 2015.

6. Z. Cai and N. Vasconcelos. Cascade R-CNN: Delving intohigh quality object detection. In Proceedings of the IEEEconference on computer vision and pattern recognition,pages 6154-6162, 2018.

7. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. SSD: Single shot multibox detector.ECCV, p. 21-37. Springer, 2016

8. C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, A. C. Berg. DSSD:Deconvolutional single shot detector. arXiv preprintarXiv:1701.06659, 2017.

9. J. Redmon, S. Divvala, R. Girshick, A. Farhadi. You onlylook once: Unified, real-time object detection. InProceedings of the IEEE conference on computer visionand pattern recognition, p. 779-788, 2016.

10. J. Redmon, A. Farhadi. Yolo9000: better, faster, stronger.In Proceedings of the IEEE conference on computer visionand pattern recognition, p. 7263-7271, 2017.

11. J. Redmon, A. Farhadi. Yolov3: An incrementalimprovement. arXiv preprint arXiv:1804.02767, 2018.

12. X. Zhou, D. Wang, P. Krähenbühl. Object as Points. arXivpreprint arXiv:1904.07850v2, 2019.

13. H. Law, J. Deng. Cornernet: Detecting objects as pairedkeypoints. In Proceedings of the European conference oncomputer vision, p. 734-750, 2018.

14. A.P. Mikhailov, A.G. Chibunichev. Photogrammetry -MIIGAIK Publishing, Moscow, 2016, 294 p. (In Russianlanguage)

15. S. A. Nene, S. K. Nayar, H. Murase. Columbia ObjectImage Library (COIL-100). Technical Report CUCS-006-96. February, 1996.

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