Fine-grained detection and segmentation of civilian aircraft in satellite imagery using YOLOv8

• Autorzy: Panneerselvam Ramesh Kumar , Bandi Sarada , Doddapaneni Sree Datta Siva Charan

Identyfikatory

DOI

10.35784/iapgos.6809

Warianty tytułu

PL

Precyzyjne wykrywanie i segmentacja samolotów cywilnych na zdjęciach satelitarnych przy użyciu YOLOv8

• Języki publikacji: EN

Abstrakty

EN

Detectionand segmentation of civilian aircraft from satellite imagery has significant importance in applications for air traffic management, surveillance, and defense. Yet, its visual confusions and lack of unification in recognition make it hard. This paper presents that by developing an efficient YOLOv8-based model for aircraft detection, classification, and segmentation within the FAIR1M-2.0 dataset. This proposed methodology involves dataset preprocessing and compatibility adjustments where the backbone used is CSPDarknet53 combining with the C2f module, which provides an efficient multi-scale representation, this happens to be the most critical requirement in distinguishing between among 11 unique categories of aircraft. Including the SAM model helps improve localization precision by achieving more accurate pixel-level segmentation. The present work effectively carried out an accurateclassification and described civilian aircraft, containing the enhanced detection and quantification capability appropriate for complex satellite-oriented aircraft analysis. These reasons make the work satisfy the fundamental requirement for very accurateidentification and evaluation of aerial images.The approach improves the accuracy and precision of aircraft classification over delicate satellite images, and thus is useful in operations for real-time surveillance and monitoring. Fine-grained classification and segmentation would then be able to effectively capture slight differences between aircraft types, which are now vital to the reliable management of airspaces. This work, therefore sets a good foundation for future development and advancement of high-resolution aerial analysis in diverse operational settings.

PL

Wykrywanie i segmentacja cywilnych samolotów na podstawie obrazów satelitarnych mają kluczowe znaczenie w zarządzaniu ruchem lotniczym, nadzorze oraz obronności. Ze względu na wizualne podobieństwa między różnymi typami samolotów oraz brak standaryzacji w rozpoznawaniu, jest to zadanie trudne. Niniejszy artykuł przedstawia efektywny model oparty na YOLOv8 do wykrywania, klasyfikacji i segmentacji samolotów w zbiorze danych FAIR1M-2.0. Zaproponowana metodologia obejmuje wstępne przetwarzanie danych i dostosowanie do zgodności, w którym wykorzystano CSPDarknet53 jako bazę, połączoną z modułem C2f, co zapewnia efektywną reprezentację wieloskalową–jest to kluczowy element przy rozróżnianiu 11 unikalnych kategorii samolotów. Włączenie modelu SAM poprawia precyzję lokalizacji, pozwalając na dokładniejszą segmentację na poziomie pikseli. Prezentowane badania pozwoliły na dokładną klasyfikację i opisanie cywilnych samolotów, zapewniając ulepszone możliwości wykrywania i analizowania obiektów na obrazach satelitarnych. Takie podejście znacznie zwiększa dokładność i precyzję klasyfikacji samolotów, co czyni je przydatnym w operacjach nadzoru i monitorowania w czasie rzeczywistym. Precyzyjna klasyfikacja i segmentacja umożliwia skuteczne rozróżnianie subtelnych różnic między typami samolotów, co jest istotne dla niezawodnego zarządzania przestrzenią powietrzną. Niniejsza praca stanowi solidną podstawę dlaprzyszłych badań nad analizą obrazów lotniczych w wysokiej rozdzielczości w różnych kontekstach operacyjnych.

Słowa kluczowe

EN

aircraft classification; fine-grained recognition; object detection; satellite imagery; segmentation; YOLOv8

PL

klasyfikacja samolotów; rozpoznawanie szczegółowe; wykrywanie obiektów; obrazy satelitarne; segmentacja; YOLOv8

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2025
• Tom: T. 15, nr 2
• Strony: 5--12
• Opis fizyczny: Bibliogr. 29 poz., fot., wykr.

Twórcy

autor

Panneerselvam Ramesh Kumar

• VelagapudiRamakrishna Siddhartha Engineering College, Department of Computer Science and Engineering, Vijayawada, India

• https://orcid.org/0000-0003-3952-7072++

autor

Bandi Sarada

• VelagapudiRamakrishna Siddhartha Engineering College, Department of Computer Science and Engineering, Vijayawada, India

• https://orcid.org/0009-0004-9279-4043++

autor

Doddapaneni Sree Datta Siva Charan

• VelagapudiRamakrishna Siddhartha Engineering College, Department of Computer Science and Engineering, Vijayawada, India

• https://orcid.org/0009-0009-1594-8607++

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