中图分类号：TP391 文献标志码：A DOI: 10.16157/j.issn.0258-7998.257150
中文引用格式： 孙扩，杨航，方思茁，等. 基于YOLOv11改进的海上小目标多光谱特征检测方法[J]. 电子技术应用，2025，51(12)：20-26.
英文引用格式： Sun Kuo，Yang Hang，Fang Sizhuo，et al. Research on a multi-spectral feature-based small target detection method for the sea based on improved YOLOv11[J]. Application of Electronic Technique，2025，51(12)：20-26.

Research on a multi-spectral feature-based small target detection method for the sea based on improved YOLOv11

Abstract： In response to the complex and changeable marine environment, such as fog, strong light reflection, low illumination at night, and the limited characteristic information of small targets at sea, a small target detection method based on YOLOv11 for marine multispectral features is proposed. By designing a dual-branch YOLOv11 model to handle the fusion of cross-modal data features and introducing a global attention mechanism module for training, the improved multispectral image small target detection model can fully utilize the multispectral features, achieving accurate detection and positioning of small target objects in multispectral images, especially performing well from the perspective of unmanned aerial vehicle (UAV) aerial photography. This cross-modal fusion method can significantly enhance the robustness and accuracy of small target detection at sea. Experiments show that the improved model can achieve an mAP@50 of 96.5% on the VTSaR dataset, an increase of 0.4% compared to YOLOv11n, providing a new solution for the detection of small targets in marine aerial unmanned search and rescue.

Key words : YOLOv11；object detection；multispectral characteristics；deep learning