Re-identifying naval vessels using novel convolutional dynamic alignment networks algorithm

• Autorzy: Roy Sudipta , Jana Dipak Kumar , Long Nguyen

Identyfikatory

DOI

10.2478/pomr-2024-0007

• Języki publikacji: EN

Abstrakty

EN

Technological innovation for re-identifying maritime vessels plays a crucial role in both smart shipping technologies and the pictorial observation tasks necessary for marine recon- naissance. Vessels are vulnerable to varying gradations of engaging in the marine environment, which is complicated and dynamic compared to the conditions on land. Fewer picture samples along with considerable similarity are characteristics of warships as a class of ship, making it more challenging to recover the identities of warships at sea. Consequently, a convolutional dynamic alignment network (CoDA-Net) re-identification framework is proposed in this research. To help the network understand the warships within the desired domain and increase its ability to identify warships, a variety of ships are employed as origin information. Simulating and testing the winning of war vessels at sea helps to increase the network’s ability to recognize complexity so that users can better handle the effects of challenging maritime environments. The impact of various types of ships as transfer items is also highlighted. The research results demonstrate that the enhanced algorithm increases the overall first hit rate (Rank1) by approximately 5.9%; it also increases the mean average accuracy (mAP) by approximately 10.7% and the correlation coefficient by 0.997%.

Słowa kluczowe

EN

warship re-identification; transfer learning; domain adaptability; CoDA-Net; con-voluted Sea environment

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2024
• Tom: nr 1
• Strony: 64--76
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Roy Sudipta

• Department of Computer Science and Engineering (Cyber Security), Haldia Institute of Technology, Haldia,
• Purba Midnapur-721657, West Bengal, India

autor

Jana Dipak Kumar

• Gangarampur College, Dakshin Dinajpur, W.B.-733124, India

• https://orcid.org/0000-0003-2297-6576

autor

Long Nguyen

• Faculty of navigation, Vietnam Maritime University, 484 Lach Tray - Kenh Duong, Le Chan - Hai Phong, Viet Nam

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).