1. SISN
  2. All Volumes and Issues
  3. Volume 15, 2024
  4. Method of Identification of Combat Vehicles Based on Yolo

Method of Identification of Combat Vehicles Based on Yolo

SISN.
2024;
Volume 15
: pp. 87 - 101
https://doi.org/10.23939/sisn2024.15.087
Authors:
  1. Mariia Nazarkevych
  2. Nazar Oleksiv
1
Lviv Polytechnic National University, Lviv, Ukraine
2
Lviv Polytechnic National University, Lviv, Ukraine

A method for recognizing contours of objects in a video data stream is proposed. Data will be uploaded using a video camera in real time and object recognition will be performed. We will use the YOLO network – a method of identifying and recognizing objects in real time. Recognized objects will be recorded in a video sequence showing the contours of the objects. The approach proposed in the project reasonably synthesizes methods of artificial intelligence, theories of computer vision on the one hand, and pattern recognition on the other; it makes it possible to obtain control influences and mathematical functions for decision-making at every moment of time with the possibility of analyzing the influence of external factors and forecasting the flow of processes, and refers to the fundamental problems of mathematical modeling of real processes. The installation of the neural network is shown in detail. The characteristics of the neural network are shown and its capabilities are substantiated. Approaches to computer vision for object extraction are shown. Well-known methods are methods of expanding areas, methods based on clustering, contour selection, and methods using a histogram. The work envisages building a system for rapid identification of combat vehicles based on the latest image filtering methods developed using deep learning methods. The time spent on identifying the machine will be 10 –20 % shorter, thanks to the developed new information technology for detecting objects in conditions of rapidly changing information.

Yolo
artificial intelligence
image recognition
machine learning
identification
Classifier
  1. Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024). Deep industrial image anomaly detection: A survey. Machine Intelligence Research, 21(1), 104–135. https://doi.org/10.1007/s11633-023-1459-z
  2. Kruger-Marais, E. (2024). Subtitling for language acquisition: Eye tracking as predictor of attention allocation in education. International Journal of Language Studies, 18(2). DOI: 10.1007/s11633-023-1459-z
  3. Li, P., Zhang, Y., Yuan, L., Xiao, H., Lin, B., & Xu, X. (2024). Efficient long-short temporal attention  network  for  unsupervised  video  object  segmentation. Pattern   Recognition, 146,   110078. DOI: 10.48550/arXiv.2309.11707
  4. Ladonia, M. S. Дослідження впливу значення порогу Non-Maximal Suppression на здатність YOLO до розпізнавання об’єктів на зображеннях низької якості. Problems of Informatization and Management, 2(74), 68–73.    https://doi.org/10.18372/2073-4751.74.17884
  5. Weber E., Vedaldi A., Bischof H., Brox T., Frahm J. M. (2020). Detecting Natural Disasters, Damage, and Incidents in the Wild. Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science. Vol. 12364, 331–350.
  6. Laroca, R., Severo, E., Zanlorensi, L. A., Oliveira, L. S., Gonçalves, G. R., Schwartz, W. R., & Menotti,D. (2018, July).  A robust real-time automatic license plate recognition based on the YOLO detector. In 2018 international joint conference on neural networks (ijcnn), 1–10. IEEE.
  7. Shinde, S., Kothari, A., & Gupta, V. (2018). YOLO based human action recognition and localization.Procedia computer science, 133, 831–838.
  8. Shinde, S., Kothari, A., & Gupta, V. (2018). YOLO based human action recognition and localization.Procedia computer science, 133, 831–838.
  9. Du, J. (2018, April). Understanding of object detection based on CNN family and YOLO. In Journal of Physics: Conference Series (Vol. 1004, p. 012029). IOP Publishing.
  10. Li, Y., Zhao, Z., Luo, Y., & Qiu, Z. (2020). Real-time pattern-recognition of GPR images with YOLO v3 implemented by tensorflow. Sensors, 20(22), 6476. https://doi.org/10.3390/s20226476
  11. Chen, H., He, Z., Shi, B., & Zhong, T. (2019). Research on recognition method of electrical components based on YOLO V3. IEEE Access, 7, 157818–157829.
  12. Kawamura, E., Kannan, K., Lombaerts, T., Stepanyan, V., Dolph, C., & Ippolito, C. A. (2024). Ground- Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility. In AIAA SciTech 2024 Forum (p. 2010). DOI: 10.2514/6.2024-2010
  13. Yin, Z., Ni, Y., Li, L., Wang, T., Wu, J., Li, Z., & Tan, D. (2024). Numerical modeling and experimental investigation of a two-phase sink vortex and its fluid-solid vibration characteristics. Journal of Zhejiang University- SCIENCE A, 25(1), 47–62. DOI: 10.1631/jzus.A2200014
  14. Wolf, T., Fridovich-Keil, D., & Jones, B. A. (2024). Mutual Information-Based Trajectory Planning for Cislunar Space Object Tracking using Successive Convexification. In AIAA SCITECH 2024 Forum (p. 0626). doi.org/10.2514/6.2024-0626
  15. Zhang, H., Zheng, D., Zhang, Y., Cao, J., Lin, W., & Ling, W. K. (2024). Quality Assessment for DIBR- synthesized Views based on Wavelet Transform and Gradient Magnitude Similarity. IEEE Transactions on Multimedia. DOI: 10.1109/TMM.2024.3356029.
  16. Qin, Q., & Chen, Y. (2024). A review of retinal vessel segmentation for fundus image analysis. Engineering Applications of Artificial Intelligence, 128, 107454. https://doi.org/10.1016/j.engappai.2023.107454
  17. Li, M., Cui, Q., Wang, X., Zhang, Y., & Xiang, Y. Ftpe-Bc: Fast Thumbnail-Preserving Image Encryption Using Block-Churning. Available at SSRN 4698446 .doi.org/10.2139/ssrn.4698446
  18. Clayton-Chubb, D., Kemp, W. W., Majeed, A., Lubel, J. S., Woods, R. L., Tran, C., ... & Roberts, S. K. (2024). Metabolic dysfunction-associated steatotic liver disease in older adults is associated with frailty and social disadvantage. Liver International, 44(1), 39–51. https://doi.org/10.1111/liv.15725
  19. Mira, E. S., Sapri, A. M. S., Aljehanı, R. F., Jambı, B. S., Bashir, T., El-Kenawy, E. S. M., & Saber, M. (2024). Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications, 14(1), 293–308.
  20. Qiao, L., Liu, K., Xue, Y., Tang, W., & Salehnia, T. (2024). A multi-level thresholding image segmentation method using hybrid Arithmetic Optimization and Harris Hawks Optimizer algorithms. Expert Systems with Applications, 241, 122316. https://doi.org/10.1016/j.eswa.2023.122316
  21. Wang, Z., Deng, Y., Zhang, Y., Tang, X., Zhou, P., Li, P., ... & Zhang, M. (2024). Fibrous whey protein mediated homogeneous and  soft-textured emulsion gels for elderly:  Enhancement of bioaccessibility for curcumin. Food Chemistry, 437, 137850. https://doi.org/10.1016/j.foodchem.2023.137850
  22. Gao, J., & Huang, Y. (2024). FP-Net: frequency-perception network with adversarial training for image manipulation localization. Multimedia Tools and Applications, 1–19. https://doi.org/10.1007/s11042-023-17914-1
  23. Su, Y., Tan, W., Dong, Y., Xu, W., Huang, P., Zhang, J., & Zhang, D. (2024). Enhancing concealed object detection in Active Millimeter Wave Images using wavelet transform. Signal Processing, 216, 109303. https://doi.org/10.1016/j.sigpro.2023.109303
  24. Bhandari, J., & Russo, D. (2024). Global optimality guarantees for policy gradient methods. Operations Research.    https://doi.org/10.1287/opre.2021.0014
  25. Qian, K., & Duan, H. C. (2024). Optical counting platform of shrimp larvae using masked k-means and a side window filter. Applied Optics, 63(6), A7–A15. https://doi.org/10.1364/AO.502868
  26. Lee, S., Kim, J., Bae, P., Lee, S., & Kim, H. (2024). Intensity Histogram-Based Reliable Image Analysis Method for Bead-Based Fluorescence Immunoassay. BioChip Journal, 1–9. https://doi.org/10.1007/s13206-023- 00137-9
  27. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 779–788.
  28. Zhang, T., Chowdhery, A., Bahl, P., Jamieson, K., & Banerjee, S. (2015, September). The design and implementation of a wireless video surveillance system. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, 426–438. https://doi.org/10.1145/2789168.2790123
  29. Nazarkevych, M., Logoyda, M., Troyan, O., Vozniy, Y., & Shpak, Z. (2019, September). The ateb-gabor filter for fingerprinting. In Conference on Computer Science and Information Technologies, 247–255. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33695-0_18
  30. Boyko, N., Kuba, M., Mochurad, L., & Montenegro, S. (2019). Fractal Distribution of Medical Data in Neural Network. In IDDM,  307–318.