The use of deep learning for military vehicle identification in SAR imagery

• Autorzy: Ślesicka Anna , Ślesicki Błażej

Identyfikatory

DOI

10.20858/sjsutst.2025.128.14

• Języki publikacji: EN

Abstrakty

EN

Artificial intelligence approaches, especially those involving deep learning, have recently become integral to object detection, as they can autonomously identify relevant features in visual datasets. The identification of military equipment, including mechanized vehicles, is crucial for threat detection and minimizing the impact of enemy actions by enabling countermeasures to be taken as quickly as possible after the threat is detected. The application of deep learning, particularly convolutional neural networks (CNN), is a highly effective tool for image processing and pattern recognition in visual data. These networks utilize convolutional layers to automatically extract features from images, making them ideal for analyzing synthetic aperture radar (SAR) imagery. Active sensor technologies like SAR are essential for object recognition due to their capability to operate in all weather conditions, both day and night.

Słowa kluczowe

EN

artificial intelligence; convolutional neural networks; SAR radar; military equipment

PL

sztuczna inteligencja; konwolucyjne sieci neuronowe; radar SAR; sprzęt wojskowy

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2025
• Tom: z. 128
• Strony: 251--268
• Opis fizyczny: Bibliogr. 23 poz.

Twórcy

autor

Ślesicka Anna

• Institute of Navigation, Polish Air Force University, Dywizjonu 303 no 35 Street, 08-521 Dęblin, Poland

• https://orcid.org/0000-0002-6313-030X

autor

Ślesicki Błażej

• Faculty of Aviation Division, Polish Air Force University, Dywizjonu 303 no 35 Street, 08-521 Dęblin, Poland

• https://orcid.org/0000-0002-0857-1081

Bibliografia

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