Advanced ship detection and ocean monitoring with satellite imagery and deep learning for marine science applications
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Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Ship detection from satellite imagery is a powerful tool in marine science, offering crucial understanding of vessel traffic patterns, fishing activities, and environmental impacts on marine ecosystems. The ability to monitor ship movements on a large scale aids in assessing anthropogenic pressures on sensitive habitats, enforcing regulatory compliance, and supporting conservation efforts in protected areas. This study offers a fresh perspective on ship detection in oceanic environments by pioneering the evaluation of YOLOv9, a cutting-edge detection algorithm, within this domain for the first time. Applying a diverse set of data augmentation techniques significantly improved the algorithm's ability to detect small ships. Additionally, atmospheric scattering effects commonly present in satellite images were mitigated through filtering, further enhancing detection performance. With a remarkable increase in speed and significantly superior performance, particularly in detecting small ships and minimizing detection time, YOLOv9 emerges as the premier candidate for real-time applications and timesensitive critical operations.
Açıklama
Anahtar Kelimeler
Ship detection, YOLOv9, Ocean monitoring, Deep learning, Maritime safety, Satellite imagery
Kaynak
Regional Studies in Marine Science
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
81
