Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.276175

Keywords:

GoogleNet, YOLO, neural network, Data Fusion, UAV recognition, optical channel, FMCW-radar

Abstract

This work reports a study into the possibility of using the GoogleNet neural network in the optoelectronic channel of the Data Fusion system. The search for the most accurate algorithms for detecting and recognizing unmanned aerial vehicles (UAVs) in Data Fusion systems has been carried out. The data processing scheme was selected (merging SVF state vectors and merging MF measurements), as well as the sensors and recognition models on each channel of the system. The Data Fusion model based on the Kalman Filter was chosen, integrating radar and optoelectronic channels. Mini-radars LPI-FMCW were used as a radar channel. Evaluation of the effectiveness of the selected Data Fusion channel model in UAV detection is based on the recognition accuracy. The main study is aimed at determining the possibility of using the GoogleNet neural network in the optoelectronic channel for UAV recognition under conditions of different range classes. The neural network for the recognition of drones was developed using transfer training technology. For training, validation, and testing of the GoogleNet neural network, a database has been built, and a special application has been developed in the MATLAB environment. The capabilities of the developed neural network were studied for 5 variants of the distance to the object. The detection objects were the Inspire 2, DJI Phantom 4 Pro, DJI F450, DU 1911 UAVs, not included in the training database. The UAV recognition accuracy by the neural network was 98.13 % at a distance of up to 5 m, 94.65 % at a distance of up to 20 m, 92.47 % at a distance of up to 20 m, 90.28 % at a distance of up to 100 m, and 88.76 % at a distance of up to 200 m. The average speed of UAV recognition by this method was 0.81 s.

Supporting Agency

  • Authors thank Dr. Claudio Palestini, officer at NATO who oversees the project. We would like to thank Professor Alessandro Figus for his technical advice in the framework of the NATO SPS G5633 project. We thank Prof. Walter for his technical contribution and tutoring to the authors (Prof. Matta is not a member of the project and had no access to the instrumental data).

Author Biographies

Vladislav Semenyuk, Manash Kozybayev North Kazakhstan University

Doctoral Student

Department of Energetic and Radioelectronics

Ildar Kurmashev, Manash Kozybayev North Kazakhstan University

Candidate of Technical Sciences

Department of Information and Communication Technologies

Alberto Lupidi, National Laboratory of Radar and Surveillance Systems

PhD, CNIT Researcher

Alessandro Cantelli-Forti, National Laboratory of Radar and Surveillance Systems

PhD, CNIT Researcher

References

  1. Shi, X., Yang, C., Xie, W., Liang, C., Shi, Z., Chen, J. (2018). Anti-Drone System with Multiple Surveillance Technologies: Architecture, Implementation, and Challenges. IEEE Communications Magazine, 56 (4), 68–74. doi: https://doi.org/10.1109/mcom.2018.1700430
  2. Liu, H., Wei, Z., Chen, Y., Pan, J., Lin, L., Ren, Y. (2017). Drone Detection Based on an Audio-Assisted Camera Array. 2017 IEEE Third International Conference on Multimedia Big Data (BigMM). doi: https://doi.org/10.1109/bigmm.2017.57
  3. Abu-Jamous, M., Maghari, A. Y. (2019). UAV Detection Model Using Histogram of Oriented Gradients and SVM. Gaza. doi: https://doi.org/10.13140/RG.2.2.26164.99207
  4. Valappil, N. K., Memon, Q. A. (2021). Vehicle Detection in UAV Videos Using CNN-SVM. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020), 221–232. doi: https://doi.org/10.1007/978-3-030-73689-7_22
  5. Dumitrescu, C., Minea, M., Ciotirnae, P. (2019). UAV Detection Employing Sensor Data Fusion and Artificial Intelligence. Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019, 129–139. doi: https://doi.org/10.1007/978-3-030-30440-9_13
  6. Diamantidou, E., Lalas, A., Votis, K., Tzovaras, D. (2019). Multimodal Deep Learning Framework for Enhanced Accuracy of UAV Detection. Computer Vision Systems, 768–777. doi: https://doi.org/10.1007/978-3-030-34995-0_70
  7. Seidaliyeva, U., Akhmetov, D., Ilipbayeva, L., Matson, E. T. (2020). Real-Time and Accurate Drone Detection in a Video with a Static Background. Sensors, 20 (14), 3856. doi: https://doi.org/10.3390/s20143856
  8. Dadboud, F., Patel, V., Mehta, V., Bolic, M., Mantegh, I. (2021). Single-Stage UAV Detection and Classification with YOLOV5: Mosaic Data Augmentation and PANet. 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). doi: https://doi.org/10.1109/avss52988.2021.9663841
  9. Mahdavi, F., Rajabi, R. (2020). Drone Detection Using Convolutional Neural Networks. 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). doi: https://doi.org/10.1109/icspis51611.2020.9349620
  10. Singha, S., Aydin, B. (2021). Automated Drone Detection Using YOLOv4. Drones, 5 (3), 95. doi: https://doi.org/10.3390/drones5030095
  11. Saqib, M., Daud Khan, S., Sharma, N., Blumenstein, M. (2017). A study on detecting drones using deep convolutional neural networks. 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). doi: https://doi.org/10.1109/avss.2017.8078541
  12. Al-Emadi, S., Al-Ali, A., Mohammad, A., Al-Ali, A. (2019). Audio Based Drone Detection and Identification using Deep Learning. 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). doi: https://doi.org/10.1109/iwcmc.2019.8766732
  13. Behera, D. K., Bazil Raj, A. (2020). Drone Detection and Classification using Deep Learning. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). doi: https://doi.org/10.1109/iciccs48265.2020.9121150
  14. Lee, D., Gyu La, W., Kim, H. (2018). Drone Detection and Identification System using Artificial Intelligence. 2018 International Conference on Information and Communication Technology Convergence (ICTC). doi: https://doi.org/10.1109/ictc.2018.8539442
  15. Hu, Y., Wu, X., Zheng, G., Liu, X. (2019). Object Detection of UAV for Anti-UAV Based on Improved YOLO v3. 2019 Chinese Control Conference (CCC). doi: https://doi.org/10.23919/chicc.2019.8865525
  16. Andraši, P., Radišić, T., Muštra, M., Ivošević, J. (2017). Night-time Detection of UAVs using Thermal Infrared Camera. Transportation Research Procedia, 28, 183–190. doi: https://doi.org/10.1016/j.trpro.2017.12.184
  17. Hammer, M., Hebel, M., Arens, M., Laurenzis, M. (2018). Lidar-based detection and tracking of small UAVs. Emerging Imaging and Sensing Technologies for Security and Defence III; and Unmanned Sensors, Systems, and Countermeasures. doi: https://doi.org/10.1117/12.2325702
  18. Spinello, L., Luber, M., Arras, K. O. (2011). Tracking people in 3D using a bottom-up top-down detector. 2011 IEEE International Conference on Robotics and Automation. doi: https://doi.org/10.1109/icra.2011.5980085
  19. Li, B., Zhang, T., Xia, T. (2016). Vehicle detection from 3D LiDAR using fully convolutional network. Robotics: Science and Systems. doi: https://doi.org/10.48550/arXiv.1608.07916
  20. Armbruster, W., Hammer, M. (2012). Segmentation, classification, and pose estimation of maritime targets in flash-ladar imagery. Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI. doi: https://doi.org/10.1117/12.974838
  21. Teo, M. I., Seow, C. K., Wen, K. (2021). 5G Radar and Wi-Fi Based Machine Learning on Drone Detection and Localization. 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS). doi: https://doi.org/10.1109/icccs52626.2021.9449224
  22. Yang, F., Xu, F., Fioranelli, F., Le Kernec, J., Chang, S., Long, T. (2021). Practical Investigation of a MIMO radar system capabilities for small drones detection. IET Radar, Sonar & Navigation, 15 (7), 760–774. doi: https://doi.org/10.1049/rsn2.12082
  23. Nuss, B., Sit, L., Fennel, M., Mayer, J., Mahler, T., Zwick, T. (2017). MIMO OFDM radar system for drone detection. 2017 18th International Radar Symposium (IRS). doi: https://doi.org/10.23919/irs.2017.8008141
  24. Beasley, P., Ritchie, M., Griffiths, H., Miceli, W., Inggs, M., Lewis, S., Kahn, B. (2020). Multistatic Radar Measurements of UAVs at X-band and L-band. 2020 IEEE Radar Conference (RadarConf20). doi: https://doi.org/10.1109/radarconf2043947.2020.9266444
  25. Quevedo, Á. D., Urzaiz, F. I., Menoyo, J. G., López, A. A. (2019). Drone detection and radar‐cross‐section measurements by RAD‐DAR. IET Radar, Sonar & Navigation, 13 (9), 1437–1447. doi: https://doi.org/10.1049/iet-rsn.2018.5646
  26. Bernard-Cooper, J., Rahman, S., Robertson, D. A. (2022). Multiple drone type classification using machine learning techniques based on FMCW radar micro-Doppler data. Radar Sensor Technology XXVI. doi: https://doi.org/10.1117/12.2618026
  27. Dhulashia, D., Peters, N., Horne, C., Beasley, P., Ritchie, M. (2021). Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight. Frontiers in Signal Processing, 1. doi: https://doi.org/10.3389/frsip.2021.781777
  28. Jajaga, E., Rushiti, V., Ramadani, B., Pavleski, D., Cantelli-Forti, A., Stojkovska, B., Petrovska, O. (2022). An Image-Based Classification Module for Data Fusion Anti-drone System. Image Analysis and Processing. ICIAP 2022 Workshops, 422–433. doi: https://doi.org/10.1007/978-3-031-13324-4_36
  29. Coluccia, A., Fascista, A., Schumann, A., Sommer, L., Dimou, A., Zarpalas, D. et al. (2022). Drone-vs-Bird Detection Challenge at ICIAP 2021. Image Analysis and Processing. ICIAP 2022 Workshops, 410–421. doi: https://doi.org/10.1007/978-3-031-13324-4_35
  30. Lupidi, A., Cantelli-Forti, A., Jajaga, E., Matta, W. (2022). An Artificial Intelligence Application for a Network of LPI-FMCW Mini-radar to Recognize Killer-drones. Proceedings of the 18th International Conference on Web Information Systems and Technologies. doi: https://doi.org/10.5220/0011590300003318
  31. Ozcan, T., Basturk, A. (2019). Lip Reading Using Convolutional Neural Networks with and without Pre-Trained Models. Balkan Journal of Electrical and Computer Engineering, 7 (2), 195–201. doi: https://doi.org/10.17694/bajece.479891
  32. Urbann, O., Stenzel, J. (2019). A Convolutional Neural Network that Self-Contained Counts. Journal of Image and Graphics, 7 (4), 112–116. doi: https://doi.org/10.18178/joig.7.4.112-116
  33. Aubakirova, G., Ivel, V., Gerassimova, Y., Moldakhmetov, S., Petrov, P. (2022). Application of artificial neural network for wheat yield forecasting. Eastern-European Journal of Enterprise Technologies, 3 (4 (117)), 31–39. doi: https://doi.org/10.15587/1729-4061.2022.259653
  34. Kaliaskarov, N., Ivel, V., Gerasimova, Y., Yugay, V., Moldakhmetov, S. (2020). Development of a distributed wireless Wi-Fi system for monitoring the technical condition of remote objects. Eastern-European Journal of Enterprise Technologies, 5 (9 (107)), 36–48. doi: https://doi.org/10.15587/1729-4061.2020.212301
Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

Downloads

Published

2023-04-29

How to Cite

Semenyuk, V., Kurmashev, I., Lupidi, A., & Cantelli-Forti, A. (2023). Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System . Eastern-European Journal of Enterprise Technologies, 2(9 (122), 16–25. https://doi.org/10.15587/1729-4061.2023.276175

Issue

Vol. 2 No. 9 (122) (2023): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2023 Vladislav Semenyuk, Ildar Kurmashev, Alberto Lupidi, Alessandro Cantelli-Forti

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.