Article: Research on a ship target detection method in remote sensing images at sea Journal: International Journal of Information and Communication Technology (IJICT) 2024 Vol.25 No.12 pp.29 - 45 Abstract: With the accelerated exploitation of marine resources, there is an increasing demand for marine surveillance, navigation safety and port management, in which ship target detection technology is particularly critical. Traditional ship detection methods rely on manual feature extraction and threshold classification, which are inadequate in the face of environmental changes. In this study, a novel ship detection algorithm is proposed, which integrates YOLOv4, convolutional block attention module and transformer mechanism, not only improves the accuracy and robustness of far-sea ship detection, but also provides a new solution strategy for remote sensing image target detection in complex scenes. The experimental data from the SSDD dataset reveal that the algorithm developed in this research surpasses current leading-edge models in both detection precision and velocity. This is particularly evident in the identification of minor targets and within intricate background scenarios, where the algorithm exhibits marked superiority. Inderscience Publishers - linking academia, business and industry through research

Title: Research on a ship target detection method in remote sensing images at sea

Authors: Weiping Zhou; Shuai Huang; Qinjun Luo; Lisha Yu

Addresses: Shipbuilding Engineering Department, Jiangxi Polytechnic University, JiuJiang, 332007 China ' Shipbuilding Engineering Department, Jiangxi Polytechnic University, JiuJiang, 332007 China ' Center for Modern Education Technology, Jiangxi Ploytechnic University, JiuJiang, 332007, China ' Shanghai Cric information Technology Co. Ltd., Shanghai, 200072, China

Abstract: With the accelerated exploitation of marine resources, there is an increasing demand for marine surveillance, navigation safety and port management, in which ship target detection technology is particularly critical. Traditional ship detection methods rely on manual feature extraction and threshold classification, which are inadequate in the face of environmental changes. In this study, a novel ship detection algorithm is proposed, which integrates YOLOv4, convolutional block attention module and transformer mechanism, not only improves the accuracy and robustness of far-sea ship detection, but also provides a new solution strategy for remote sensing image target detection in complex scenes. The experimental data from the SSDD dataset reveal that the algorithm developed in this research surpasses current leading-edge models in both detection precision and velocity. This is particularly evident in the identification of minor targets and within intricate background scenarios, where the algorithm exhibits marked superiority.

Keywords: ship target detection; remote sensing images; YOLOv4; attention mechanism.

DOI: 10.1504/IJICT.2024.143631

International Journal of Information and Communication Technology, 2024 Vol.25 No.12, pp.29 - 45

Received: 27 Oct 2024
Accepted: 21 Nov 2024
Published online: 02 Jan 2025 *

Title: Research on a ship target detection method in remote sensing images at sea

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