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The Hough transform in the classification process of inland ships

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Języki publikacji
EN
Abstrakty
EN
This article presents an analysis of the possibilities of using image processing methods for feature extraction that allows kNN classification based on a ship’s image delivered from an on-water video surveillance system. The subject of the analysis is the Hough transform which enables the detection of straight lines in an image. The recognized straight lines and the information about them serve as features in the classification process. Above all, this approach allows ships to be recognized, which can then be characterized by a specific representation and shape. Recreational units that are often seen on inland waters were classified correctly using this method. Each analyzed camera image was previously prepared – brought to the form where the ship was visible from the side and the background removed (they were monochromatic – white). The results obtained in this work will allow for the development of the final ship classification method based on camera images. This method is a significant part of the emerging system prototype, which is implemented as part of the Automatic Ship Recognition and Identification (SHREC) project.
Rocznik
Strony
9--15
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
  • Gdańsk University of Technology, Faculty of Civil and Environmental Engineering 11/12 Gabriela Narutowicza St., 80-233 Gdańsk, Poland
  • Marine Technology Sp. z o.o. 4/6 Roszczynialskiego St., 81-521 Gdynia, Poland
Bibliografia
  • 1. Akiyama, T., Kobayashi, Y., Kishigami, J. & Muto, K. (2018) CNN-Based Boat Detection Model for Alert System Using Surveillance Video Camera. In: 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018, 8574704, Institute of Electrical and Electronics Engineers Inc., pp. 758–759. 7th IEEE Global Conference on Consumer Electronics, GCCE 2018, Nara, Japan, 18/10/9. https:// doi.org/10.1109/GCCE.2018.8574704
  • 2. Ali, M., Radzi, A. & Saad, H.M. (2017) A new approach to highway lane detection by using Hough transform technique. Journal of ICT 16 (2), pp. 244–260.
  • 3. Bobkowska, K. (2016) Analysis of the objects images on the sea using Dempster-Shafer theory. 17th International Radar Symposium (IRS), 10–12 May 2016, Kraków, Poland. IEEE, 1–4. DOI: 10.1109/IRS.2016.7497280.
  • 4. Cohen, A.E., Cavallo, S.M., Coniglio, M.C. & Brooks, H.E. (2015) A Review of Planetary Boundary Layer Parameterization Schemes and Their Sensitivity in Simulating Southeast U.S. Cold Season Severe Weather Environment. Weather and Forecasting 30 (3), pp. 591–612.
  • 5. Ferreira, J.C., Branquinho, J., Ferreira, P.C. & Piedade, F. (2017) Computer Vision Algorithms Fishing Vessel Monitoring Identification of Vessel Plate Number. In: De Paz J., Julián V., Villarrubia G., Marreiros G., Novais P. (eds) Ambient Intelligence – Software and Applications – 8th International Symposium on Ambient Intelligence (ISAmI 2017). ISAmI 2017. Advances in Intelligent Systems and Computing 615. Springer, Cham, pp. 9–17.
  • 6. Hyla, T. & Wawrzyniak, N. (2019) Automatic Ship Detection on Inland Waters: Problems and a Preliminary Solution. In Proceedings of ICONS 2019 The Fourteenth International Conference on Systems, IARIA, Valencia, Spain (pp. 56–60).
  • 7. Kanjir, U., Greidanus, H. & Oštir, K. (2018) Vessel detection and classification from spaceborne optical images: A literature survey. Remote Sensing of Environment 207, pp. 1–26.
  • 8. Koc-San, D., Selim, S., Aslan, N. & San, B.T. (2018) Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform. Computers and Electronics in Agriculture 150, pp. 289–301.
  • 9. Meng, Y., Zhang, Z., Yin, H. & Ma, T. (2018) Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron 106, pp. 34–41.
  • 10. Rawat, W. & Wang, Z. (2017) Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation 29 (9), pp. 2352–2449.
  • 11. Shao, Z., Wang, L., Wang, Z., Du, W. & Wu, W. (2019) Saliency-Aware Convolution Neural Network for Ship Detection in Surveillance Video. IEEE Transactions on Circuits and Systems for Video Technology. DOI: https://doi. org/10.1109/TCSVT.2019.2897980
  • 12. Shrivakshan, G.T. & Chandrasekar, C. (2012) A Comparison of various Edge Detection Techniques used in Image Processing. IJCSI International Journal of Computer Science Issues 9, 5 (1), pp. 269–276.
  • 13. Solmaz, B., Gundogdu, E., Yucesoy, V., Koç, A. & Alatan, A.A. (2018) Fine-grained recognition of maritime vessels and land vehicles by deep feature embedding. IET Computer Vision 12 (8), pp. 1121–1132.
  • 14. Turker, M. & Koc-San, D. (2015) Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. International Journal of Applied Earth Observation and Geoinformation 34, pp. 58–69.
  • 15. Wang, C., Jiang, S., Zhang, H., Wu, F. & Zhang, B. (2014) Ship detection for high-resolution SAR images based on feature analysis. IEEE Geoscience and Remote Sensing Letters 11 (1), pp. 119–123.
  • 16. Wawrzyniak, N. & Hyla, T. (2019) Automatic Ship Identification Approach for Video Surveillance Systems. In Proceedings of ICONS 2019 The Fourteenth International Conference on Systems, IARIA, Valencia, Spain (pp. 65–68).
  • 17. Wawrzyniak, N. & Stateczny, A. (2018) Automatic Watercraft Recognition and Identification on Water Areas Covered by Video Monitoring as Extension for Sea and River Traffic Supervision Systems. Polish Maritime Research 25 (s1), pp. 5–13.
  • 18. Zhang, M.M., Choi, J., Daniilidis, K., Wolf, M.T. & Kanan, C. (2015) Vais: A dataset for recognizing maritime imagery in the visible and infrared spectrums. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 7–12 June 2015, Boston, MA USA, pp. 10–16.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-400022f2-f7ad-4def-81a7-035a46a396b5
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