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Liczba wyników
2024 | No. 29 | 90--99
Tytuł artykułu

Automatic aircraft recognition using convolutional neural Network

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the possibility of utilisation convolutional neural network for aircraft classification by their type. Main purpose of the study was to use a common deep learning network and modify it to correctly classify three types of general aviation aircraft. Differentiation is made based on their low quality picture with black outline on white background. Database utilized in this study is small compared to common CNN databases and results ought to be taken only as a trend. Research consisted of choosing right parameters of network to make the recognition as accurate as possible. 20 samples have been produced to evaluate accuracy of the software and eliminate deviations. Conclusions and issues have also been described.
Słowa kluczowe
Wydawca

Rocznik
Tom
Strony
90--99
Opis fizyczny
Bibliogr. 15 poz., rys., wykr.
Twórcy
  • Polish Air Force University, Institute of Navigation Dywizjonu 303 no 35 Street, 08-521 Dęblin, Poland, d.adamiak6888@wsosp.edu.pl
  • Polish Air Force University, Institute of Navigation Dywizjonu 303 no 35 Street, 08-521 Dęblin, Poland, a.slesicka@law.mil.pl
Bibliografia
  • 1. Y. Li, Y. Xiao, Y. Gong, R. Zhang, Y. Huo, Y. Wu. Explainable AI: A Way to Achieve Trustworthy AI. IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS), 2024.
  • 2. Q. Lu, L. Zhu, X. Xu, J. Whittle, D. Douglas, C. Sanderson. Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns. IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP). 2022.
  • 3. K. Tejasen, P. Raju, W. Ryan, A. Jordan, R. Yang. Defining an Initial Classification Scheme for Non-Deterministic AI Technologies. IEEE Integrated Communications, Navigation and Surveillance Conference, ICNS, 2022.
  • 4. Y. Camgözlü, Y. Kutlu. Analysis of Filter Size Effect in Deep Learning. Journal of Artificial Intelligence with Applications, 1(1), 20-29, 2020.
  • 5. A. Passah, S. Sur, B. Paul, D. Kandar. SAR Image Classification: A comprehensive Study and Analysis. IEEE Access, 2022.
  • 6. Y. Wang, H. Li, P. Jia, G. Zhang, T. Wang, X. Hao. Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images. Sensors 2019.
  • 7. F. Li, Z. Wu, J. Li, Z. Lai, B. Zhao, C. Min. A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles. Sensors, 2021.
  • 8. J. Li, Z. Yu, J. Chen, H. Jiang. A SAR Ship Detection Method Based on Adversarial Training. Naval Submarine Academy, Qingdao 264001, China, 2024.
  • 9. J. Iu, C. Xu, H. Su, L. Gao, T. Wang. Deep Learning for SAR Ship Detection: Past, Present and Future. Remote Sens 2022.
  • 10. D. Chae, H. Lim, J. Park, Y. Choi, J. hee Yoo. Application of Satellite SAR Image to Ground Vehicle Target Dataset and Detection and Object Segmentation”. Journal of the KIMST, 25(1):30–44, 2022.
  • 11. T. Hiippala. Recognizing Military Vehicles in Social Media Images Using Deep Learning. IEEE, 2017.
  • 12. F. Chen, A. Zhang, H. Balzter, P. Ren, H. Zhou. Oil Spill SAR Image Segmentation via Probability Distribution Modelling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:533–554, 2022.
  • 13. B. Choe. Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in The Canadian Arctic. 2017.
  • 14. Y. Liu, H. Pu, D. Sun. Efficient Extraction of Deep Image Features Using Convolutional Neural Network (CNN) for Applications in Detecting and Analyzing Complex Food Matrices, Trends in Food Science & Technology, 2021.
  • 15. P. Lang, X. Fu, M. Martorella, J. Dong, R. Qin, X. Meng, M. Xie. A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing. 2020.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-5dff2a01-f462-4eb9-adf8-eb738a8848dc
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