Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods

Furkan Buyukkanber; Mustafa Yanalak; Nebiye Musaoglu

doi:10.1109/RAST57548.2023.10197948

Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods

Furkan Buyukkanber, Mustafa Yanalak, Nebiye Musaoglu

Geomatik Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

Vessel detection from remote sensing images is becoming exponentially crucial component in marine surveillance applications including maritime traffic control, anti-illegal fishing applications, oil discharge control, marine pollution and safety. Applying deep learning methods to vessel detection applications ineluctably improve the detection results and overcome unforeseen errors that could be made by analysts. Publicly available datasets play vital role for development and evaluation process of deep learning models. In this paper, open source DOTA dataset has been revised and trained with single-staged deep learning methods. The results show that YOLOv8 model has the most efficient value on detecting ships and fastest to detect instances from given test images on inference.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9798350323023
DOI'lar	https://doi.org/10.1109/RAST57548.2023.10197948
Yayın durumu	Yayınlandı - 2023
Etkinlik	10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023 - Istanbul, Turkey Süre: 7 Haz 2023 → 9 Haz 2023

Yayın serisi

Adı	Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	7/06/23 → 9/06/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Finansman

We would like to express our sincere thanks to Istanbul Technical University Center for Satellite Communication and Remote Sensing (ITU-CSCRS) for the support given.

Finansörler	Finansör numarası
Istanbul Technical University Center for Satellite Communication and Remote Sensing

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

Belgeye Erişim

10.1109/RAST57548.2023.10197948

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Buyukkanber, F., Yanalak, M., & Musaoglu, N. (2023). Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods. In Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023 (Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAST57548.2023.10197948

Buyukkanber, Furkan ; Yanalak, Mustafa ; Musaoglu, Nebiye. / Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods. Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023).

@inproceedings{58d749f59e5e4e678a8c180a39428089,

title = "Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods",

abstract = "Vessel detection from remote sensing images is becoming exponentially crucial component in marine surveillance applications including maritime traffic control, anti-illegal fishing applications, oil discharge control, marine pollution and safety. Applying deep learning methods to vessel detection applications ineluctably improve the detection results and overcome unforeseen errors that could be made by analysts. Publicly available datasets play vital role for development and evaluation process of deep learning models. In this paper, open source DOTA dataset has been revised and trained with single-staged deep learning methods. The results show that YOLOv8 model has the most efficient value on detecting ships and fastest to detect instances from given test images on inference.",

keywords = "YOLO series, convolutional neural networks, deep learning, optical remote sensing images, ship dataset, vessel detection",

author = "Furkan Buyukkanber and Mustafa Yanalak and Nebiye Musaoglu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023 ; Conference date: 07-06-2023 Through 09-06-2023",

year = "2023",

doi = "10.1109/RAST57548.2023.10197948",

language = "English",

series = "Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023",

address = "United States",

}

Buyukkanber, F, Yanalak, M & Musaoglu, N 2023, Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods. in Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023. Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023, Institute of Electrical and Electronics Engineers Inc., 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023, Istanbul, Turkey, 7/06/23. https://doi.org/10.1109/RAST57548.2023.10197948

Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods. / Buyukkanber, Furkan; Yanalak, Mustafa ; Musaoglu, Nebiye.
Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods

AU - Buyukkanber, Furkan

AU - Yanalak, Mustafa

AU - Musaoglu, Nebiye

PY - 2023

Y1 - 2023

N2 - Vessel detection from remote sensing images is becoming exponentially crucial component in marine surveillance applications including maritime traffic control, anti-illegal fishing applications, oil discharge control, marine pollution and safety. Applying deep learning methods to vessel detection applications ineluctably improve the detection results and overcome unforeseen errors that could be made by analysts. Publicly available datasets play vital role for development and evaluation process of deep learning models. In this paper, open source DOTA dataset has been revised and trained with single-staged deep learning methods. The results show that YOLOv8 model has the most efficient value on detecting ships and fastest to detect instances from given test images on inference.

AB - Vessel detection from remote sensing images is becoming exponentially crucial component in marine surveillance applications including maritime traffic control, anti-illegal fishing applications, oil discharge control, marine pollution and safety. Applying deep learning methods to vessel detection applications ineluctably improve the detection results and overcome unforeseen errors that could be made by analysts. Publicly available datasets play vital role for development and evaluation process of deep learning models. In this paper, open source DOTA dataset has been revised and trained with single-staged deep learning methods. The results show that YOLOv8 model has the most efficient value on detecting ships and fastest to detect instances from given test images on inference.

KW - YOLO series

KW - convolutional neural networks

KW - deep learning

KW - optical remote sensing images

KW - ship dataset

KW - vessel detection

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U2 - 10.1109/RAST57548.2023.10197948

DO - 10.1109/RAST57548.2023.10197948

M3 - Conference contribution

AN - SCOPUS:85168413254

T3 - Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023

BT - Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023

Y2 - 7 June 2023 through 9 June 2023

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Buyukkanber F, Yanalak M , Musaoglu N. Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods. In Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023). doi: 10.1109/RAST57548.2023.10197948

Vessel Detection from Optical Remote Sensing Images with Deep Learning Methods

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap