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<p>With the increasing demand for large-scale maritime security and ocean surveillance, infrared ship target detection systems are required to process massive infrared data streams with high accuracy and real-time performance. However, the small size of ship targets, low contrast in infrared imagery, and interference from complex sea backgrounds pose significant challenges to both detection accuracy and computational efficiency, limiting the practical deployment of existing methods in high-performance or distributed computing environments. To address these challenges, this paper proposes an efficient infrared ship detection framework, termed AAF-YOLO (ADown+AKConv-SE+FLAHead-YOLO), which is designed to achieve a favorable balance between detection performance and computational cost, making it suitable for real-time inference and parallel acceleration on modern supercomputing platforms. Experimental results on an infrared ship dataset demonstrate that AAF-YOLO achieves a mAP of 90.9% with only 9&#xa0;M parameters, improving mAP by 3.1% while reducing the number of parameters by 18.7% compared with YOLOv8s, highlighting its applicability to supercomputing-enabled intelligent maritime surveillance systems.</p>

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Enhanced infrared ship detection via AAF-YOLO: a lightweight and accurate approach

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Ziling Li,
  • Danhong Zhang,
  • Ming Xu,
  • Yixin Su,
  • Zihao Xu

摘要

With the increasing demand for large-scale maritime security and ocean surveillance, infrared ship target detection systems are required to process massive infrared data streams with high accuracy and real-time performance. However, the small size of ship targets, low contrast in infrared imagery, and interference from complex sea backgrounds pose significant challenges to both detection accuracy and computational efficiency, limiting the practical deployment of existing methods in high-performance or distributed computing environments. To address these challenges, this paper proposes an efficient infrared ship detection framework, termed AAF-YOLO (ADown+AKConv-SE+FLAHead-YOLO), which is designed to achieve a favorable balance between detection performance and computational cost, making it suitable for real-time inference and parallel acceleration on modern supercomputing platforms. Experimental results on an infrared ship dataset demonstrate that AAF-YOLO achieves a mAP of 90.9% with only 9 M parameters, improving mAP by 3.1% while reducing the number of parameters by 18.7% compared with YOLOv8s, highlighting its applicability to supercomputing-enabled intelligent maritime surveillance systems.