Home Journals Articles Updates About
CN
image
收藏切换
Remote sensing image ship target detection algorithm based on improved YOLOv7
收藏切换
PDF
Ziwei DONG1, Hongxiang REN1, 2, Xiao YANG2, Haina TANG3, Jiaqi ZHENG2
Navigation of China | 2025, 48(3) : 137 - 146
Less
收藏切换
Navigation of China | 2025, 48(3): 137-146
• Intelligent Shipping •
Remote sensing image ship target detection algorithm based on improved YOLOv7
Full
Ziwei DONG1, Hongxiang REN1, 2, Xiao YANG2, Haina TANG3, Jiaqi ZHENG2
Affiliations
  • 1.College of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China
  • 2.Navigation College, Dalian Maritime University, Dalian 116026, China
  • 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Published: 2025-09-25 doi: 10.3969/j.issn.1000-4653.2025.03.017
Outline
收藏切换

To address challenges such as significant scale variations, high aspect ratios, dense arrangements, and complex backgrounds in ship target detection from remote sensing images, this paper proposes an improved YOLOv7-based algorithm. Using YOLOv7 as the baseline network, the prior anchor generation algorithm is optimized for the dataset. A long-edge representation method combined with circular smooth labeling is introduced to detect ship targets with uncertain rotation directions. The YOLOv7 network is enhanced by embedding both the GAM (Global Attention Mechanism) and SimAM (Simple Attention Mechanism) modules, which effectively suppress interference from complex background regions in remote sensing images and improve target detection accuracy. Additionally, the coordinate loss function is optimized to accelerate model convergence. Experimental results on the DOTA-ship and HRSC2016 datasets for both single-class and multi-class detection tasks show mAP values of 86.1%, 97.7%, and 87.1%, respectively-representing improvements of 7.8%, 4.6%, and 7.9% over the original YOLOv7 model. These results validate the effectiveness and superiority of the proposed method.

remote sensing images  /  ship target  /  YOLOv7  /  attention mechanisms
Ziwei DONG, Hongxiang REN, Xiao YANG, Haina TANG, Jiaqi ZHENG. Remote sensing image ship target detection algorithm based on improved YOLOv7[J]. Navigation of China, 2025 , 48 (3) : 137 -146 . DOI: 10.3969/j.issn.1000-4653.2025.03.017
Appendix
Less
Year 2025 volume 48 Issue 3
PDF
60
17
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1000-4653.2025.03.017
  • Receive Date：2024-07-29
  • Online Date：2026-03-17
  • Published：2025-09-25
Article Data
Affiliations
History
  • Received：2024-07-29
Funding
Affiliations
    1.College of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China
    2.Navigation College, Dalian Maritime University, Dalian 116026, China
    3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
References
Share
https://castjournals.cast.org.cn/joweb/zghh/EN/10.3969/j.issn.1000-4653.2025.03.017
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House