Assessing YOLOv8 Performance and Limitations in Aircraft and Ship Detection from Satellite Images
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Accurate detection of aircraft and ships from remote sensing images is crucial for various applications such as maritime surveillance, border security, and disaster management. In this study, we present a comprehensive evaluation of the performance of the YOLOv8 model in detecting aircraft and ships under diverse environmental conditions. Through extensive testing and analysis, we highlight the algorithm's capabilities and limitations in discerning objects amidst challenging backgrounds, varying lighting conditions, and complex contextual factors. Our results reveal notable successes in both aircraft and ship detection, alongside inherent challenges such as color similarity, shadow formations, and environmental turbulence. Additionally, performance metrics illustrate the model's superior efficacy in ship detection compared to aircraft detection, indicating the algorithm's adaptability to different object categories. This study contributes key insights into the capabilities of object detection algorithms in remote sensing applications, paving the way for further advancements in the field of aerial and maritime surveillance. © 2024 IEEE.
Açıklama
IEEE SMC; IEEE Turkiye Section
2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562
2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562
Anahtar Kelimeler
deep learning, object detection, satellite imagery, YOLOv8
Kaynak
2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024
WoS Q Değeri
Scopus Q Değeri
N/A
