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| Deep Learning-Based Infrared Ship Target Wake Matching and Detection Algorithm |
| CHEN Liangwen1， ZHU Yuxin1， SHEN Tao2， YU Yifan1，
LING Xiao1， SHENG Qinghong1 |
| 1. College of Astronautics，Nanjing University of Aeronautics and Astronautics， Nanjing 210016， Jiangsu， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China |
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Abstract This paper proposes a deep learning-based infrared ship-and-wake detection algorithm to address missed and false detections of low-emission, small targets in complex sea-sky backgrounds. The algorithm enhanced the YOLO network by incorporating a dual attention mechanism that suppresses feature maps within the YOLOv8 architecture. In addition, a ship-wake matching module was developed, leveraging more prominent wake features to assist ship detection, effectively reducing false alarms and missed detections of weak and small ships in complex backgrounds. Finally, a ship dataset was constructed for testing and analysis. Results show that the proposed algorithm achieves 98% precision and a high detection speed, demonstrating strong robustness in detecting weak infrared ship targets and significantly enhancing detection performance.
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Received: 05 December 2025
Published: 11 March 2026
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2026, Vol. 9 
