Home Journals Articles Updates About
CN
image
收藏切换
Target Detection Algorithm for Ship Infrared Images Based on Improved YOLO11n
收藏切换
PDF
Peng PENG1, 2, 3, Cifa CHEN2, 3, 4, *, Shang ZHANG1, 2, 3
Radio Engineering | 2025, 55(11) : 2174 - 2183
Less
收藏切换
Radio Engineering | 2025, 55(11): 2174-2183
• Signal and Information Processing •
Target Detection Algorithm for Ship Infrared Images Based on Improved YOLO11n
Full
Peng PENG1, 2, 3, Cifa CHEN2, 3, 4, *, Shang ZHANG1, 2, 3
Affiliations
  • 1.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China
  • 2.Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University, Yichang 443002, China
  • 3.College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
  • 4.Big Data Research Center, Jingchu University of Technology, Jingmen 448001, China
Published: 2025-11-05 doi: 10.3969/j.issn.1003-3106.2025.11.005
Outline
收藏切换

A ship infrared image target detection algorithm based on YOLO11n, named AGT-YOLO, is proposed to address the issues of low model accuracy and recall rate, difficulties in identifying small targets, and multi-scale recognition challenges under complex sea conditions. By introducing an improved GhostHGNetv2 network, the background discrimination capability is enhanced; the designed ASFP2 optimized neck network improves detection capabilities for low-resolution images and very small targets; the proposed Tack Adaptive Alignment Detection Head ( TAADH ) replaces the original detection head, enhancing localization and classification performance;meanwhile, the AFGCAttention mechanism is integrated to improve global information processing capability and the model's generalization ability. Experimental results show that compared to the baseline model YOLO11n, AGT-YOLO achieves a 4.4% increase in recall rate and a 3.1% increase in mean average precision at IoU=0.5 ( mAP@ 50), demonstrating strong multi-scale recognition capability and robustness in complex environments.

object detection  /  YOLO11n  /  infrared ship inspection  /  multi-scale model
Peng PENG, Cifa CHEN, Shang ZHANG. Target Detection Algorithm for Ship Infrared Images Based on Improved YOLO11n[J]. Radio Engineering, 2025 , 55 (11) : 2174 -2183 . DOI: 10.3969/j.issn.1003-3106.2025.11.005
Appendix
Less
Year 2025 volume 55 Issue 11
PDF
95
44
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1003-3106.2025.11.005
  • Receive Date：2025-07-17
  • Online Date：2026-04-17
  • Published：2025-11-05
Article Data
Affiliations
History
  • Received：2025-07-17
Affiliations
    1.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China
    2.Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University, Yichang 443002, China
    3.College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
    4.Big Data Research Center, Jingchu University of Technology, Jingmen 448001, China
References
Share
https://castjournals.cast.org.cn/joweb/wxdgc/EN/10.3969/j.issn.1003-3106.2025.11.005
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