Application of kohonen neural networks to search for regions of interest in the detection and recognition of objects

Authors

  • Victor Skuratov Joint-stock company All-Russian Scientific Research Institute of Radio Engineering Bol'shaya Pochtovaya str., 22, Moscow, Russian Federation, 105082, Russian Federation https://orcid.org/0000-0003-1526-1505
  • Konstantin Kuzmin University of Russian Innovation Education Krasnobogatyrskaya str., 10, Moscow, Russian Federation, 107061, Russian Federation https://orcid.org/0000-0003-3823-7268
  • Igor Nelin Moscow Aviation Institute Volokolamskoe highway, 4, Moscow, Russian Federation, 125993, Russian Federation https://orcid.org/0000-0003-0469-6650
  • Mikhail Sedankin Main Research and Testing Robotics Centre of the Ministry of Defence of the Russian Federation Seregina str., 5, Moscow, Russian Federation, 125167 State Research Center – Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency Marshala Novikova str., 23, Moscow, Russian Federation, 123098, Russian Federation https://orcid.org/0000-0001-9875-6313

DOI:

https://doi.org/10.15587/1729-4061.2019.166887

Keywords:

image recognition, self-organizing maps, Kohonen neural network, radar and satellite images, region of interest, ROI, test operations procedure, robotics.

Abstract

One of the most effective ways to improve accuracy and speed of recognition algorithms is to preliminary distinguish the regions of interest in the analyzed images. We studied a possibility of application of self-organizing maps and a Kohonen neural network for detection of regions of interest at a radar or satellite image of underlying surface. There is a high probability of finding an object of interest for further analysis in the found regions of interest. The definition of region of interest is necessary most of all to automate and speed up the process of search and recognition of objects of interest. The relevance is due to the increasing number of satellites. The study presents the process of modeling, analysis and comparison of the results of application of these methods for determination of regions of interest in recognition of images of aircraft against the background of underlying surface. It also describes the process of preliminary processing of input data. The study presents a general approach to construction and training of the Kohonen self-organizing map and neural network. Application of Kohonen maps and neural network makes it possible to decrease an amount of data analyzed by 15–100 times. It speeds up the process of detection and recognition of an object of interest. Application of the above algorithm reduces significantly the required number of training images for a convolutional network, which performs the final recognition. The reduction of a training sample occurs because the size of parts of an input image supplied to the convolutional network is bounded with the scale of an image and it is equal to the size of the largest detected object. Kohonen neural network showed itself more efficient in relation to this task, since it places cluster centers on the underlying surface rarely due to independence of weight of neurons on neighboring centers. These technical solutions could be used in the analysis of visual data from satellites, aircraft, and unmanned cars, in medicine, robotics, etc.

Author Biographies

Victor Skuratov, Joint-stock company All-Russian Scientific Research Institute of Radio Engineering Bol'shaya Pochtovaya str., 22, Moscow, Russian Federation, 105082

Engineer, Researcher

Konstantin Kuzmin, University of Russian Innovation Education Krasnobogatyrskaya str., 10, Moscow, Russian Federation, 107061

Researcher

Department of Mathematical and Instrumental Methods in Economics

Igor Nelin, Moscow Aviation Institute Volokolamskoe highway, 4, Moscow, Russian Federation, 125993

PhD, Associate Professor

Department of Radiolocation, Radio Navigation and On-Board Radio Electronic Equipment

Mikhail Sedankin, Main Research and Testing Robotics Centre of the Ministry of Defence of the Russian Federation Seregina str., 5, Moscow, Russian Federation, 125167 State Research Center – Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency Marshala Novikova str., 23, Moscow, Russian Federation, 123098

PhD, Senior Researcher

References

  1. Simard, P. Y., Steinkraus, D., Platt, J. C. (2003). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the Seventh International Conference on Document Analysis and Recognition. Available at: http://cognitivemedium.com/assets/rmnist/Simard.pdf
  2. Novikova, N. M., Dudenkov, V. M. (2015). Modelirovanie neyronnoy seti dlya raspoznavaniya izobrazheniy na osnove gibridnoy seti i samoorganizuyuschihsya kart Kohonena. Aspirant, 2, 31–34.
  3. Agayan, K. Yu., Hanzhin, V. G. (2018). Neyronnaya set' s arhitekturoy Kohonena dlya raspoznavaniya izobrazheniy. Prochnost' neodnorodnyh struktur – PROST 2018. Sbornik trudov IX-oy Еvraziyskoy nauchno-prakticheskoy konferencii, 153.
  4. Gerasimova, N. I., Verhoturova, A. E. (2014). Poisk fragmenta izobrazheniya s ispol'zovaniem neyronnoy seti Kohonena. Informacionnye tekhnologii v nauke, upravlenii, social'noy sfere i medicine: sbornik nauchnyh trudov Mezhdunarodnoy konferencii s mezhdunarodnym uchastiem. Ch. 1. Tomsk, 68–70.
  5. Soldatova, O. P., Chayka, P. D. (2015). Efficiency analysis of solution of classification using hybrid kohonen neural networks. Izvestiya Samarskogo nauchnogo centra Rossiyskoy akademii nauk, 17 (2), 1147–1152.
  6. Narushev, I. R. (2018). Neural network on the basis of the self-organizing kochonen card as a means of detecting anomalous behavior. Ohrana, bezopasnost', svyaz', 2 (3 (3)), 194–197.
  7. Kajan, S., Sekaj, I., Lajtman, M. Cluster Analysis Aplications in Matlab Using Kohonen Network. Available at: https://pdfs.semanticscholar.org/a8ba/6977dce4bdbeec3dd370eb614de2c6f56514.pdf
  8. LeCun, Y., Bengio, Y. (1998). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 255–258.
  9. LeCun, Y., Kavukcuoglu, K., Farabet, C. (2010). Convolutional networks and applications in vision. Proceedings of 2010 IEEE International Symposium on Circuits and Systems. doi: https://doi.org/10.1109/iscas.2010.5537907
  10. Girshick, R., Donahue, J., Darrell, T., Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition. doi: https://doi.org/10.1109/cvpr.2014.81
  11. Girshick, R., Donahue, J., Darrell, T., Malik, J. (2016). Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (1), 142–158. doi: https://doi.org/10.1109/tpami.2015.2437384
  12. Girshick, R. (2015). Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV). doi: https://doi.org/10.1109/iccv.2015.169
  13. Ren, S. et. al. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 91–99.
  14. He, K., Gkioxari, G., Dollar, P., Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). doi: https://doi.org/10.1109/iccv.2017.322
  15. Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi: https://doi.org/10.1109/cvpr.2016.91
  16. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. Computer Vision – ECCV 2016, 21–37. doi: https://doi.org/10.1007/978-3-319-46448-0_2
  17. Haykin, S. (2008). Neyronnye seti: polnyy kurs. Moscow: Izdatel'skiy dom Vil'yams.
  18. Kohonen, T. (2001). Self-organizing maps. Vol. 30. Springer Science & Business Media, 502. doi: https://doi.org/10.1007/978-3-642-56927-2
  19. Gersho, A., Gray, R. M. (1992). Vector quantization and signal compression. Vol. 159. Springer Science & Business Media, 732. doi: https://doi.org/10.1007/978-1-4615-3626-0

Downloads

Published

2019-05-13

How to Cite

Skuratov, V., Kuzmin, K., Nelin, I., & Sedankin, M. (2019). Application of kohonen neural networks to search for regions of interest in the detection and recognition of objects. Eastern-European Journal of Enterprise Technologies, 3(9 (99), 41–48. https://doi.org/10.15587/1729-4061.2019.166887

Issue

Vol. 3 No. 9 (99) (2019): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2019 Victor Skuratov, Konstantin Kuzmin, Igor Nelin, Mikhail Sedankin

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.