基于图卷积网络的红外目标检测算法
收稿日期: 2025-10-10
修回日期: 2025-11-25
录用日期: 2025-12-19
网络出版日期: 2025-12-25
基金资助
国家留学基金(［2022］20)
Infrared target detection algorithm based on graph convolutional network
Received date: 2025-10-10
Revised date: 2025-11-25
Accepted date: 2025-12-19
Online published: 2025-12-25
Supported by
National Scholarship Foundation of China(［2022］20)
红外目标因背景干扰强、纹理信息弱及结构特征模糊等特性导致其检测精度受限，且现有基于深度学习的方法多依赖单目标特征，忽略了目标间的关联信息。为此，提出一种结合类间关联和语义相似性的图卷积网络红外目标检测算法，其核心在于构建动态融合的图结构。首先，利用目标在图像中的共现概率构建静态的共现邻接矩阵，以捕获目标之间的上下文关系。同时，基于预训练的词向量构建语义邻接矩阵，并在训练过程中对其进行更新，使其能够动态适应目标标签的语义特性。随后，将2类邻接矩阵进行融合，并以类别词向量作为节点特征输入图卷积网络，从而实现对类别关系的高阶建模。最终，所获得的关系特征通过注意力机制与YOLO11主干网络提取的特征进行融合，用于后续检测头的分类分支和定位分支。在自制红外飞机数据集上得到的实验结果表明，与YOLO11相比，在计算量和参数量相当的条件下，所提算法将mAP50和mAP50：95分别提升了1.08%和0.86%，与其他最新的SOTA算法相比，所提算法的检测精度同样达到了最优。理论分析和实验结果共同验证了所提算法在复杂环境下执行红外目标检测任务的有效性。
仝照亚 , 刘刚 , 霍元智 , 樊肖亮 , 吕书贤 . 基于图卷积网络的红外目标检测算法[J]. 航空学报, 2026 , 47(12) : 332886 -332886 . DOI: 10.7527/S1000-6893.2025.32886
Infrared target detection is limited by strong background interference， weak texture information， and blurred structural features. Existing deep learning-based methods mostly rely on single-target features， neglecting inter-target correlation information. To address this issue， this paper proposes a graph convolutional network-based infrared target detection algorithm that integrates inter-class correlation and semantic similarity， with the core being the construction of a dynamically fused graph structure. Firstly， a static co-occurrence adjacency matrix is constructed using the co-occurrence probability of targets in the image to capture the contextual relationship between targets. Simultaneously， a semantic adjacency matrix is constructed based on pre-trained word vectors and updated during training to dynamically adapt to the semantic characteristics of target labels. Subsequently， the two adjacency matrices are fused， and the category word vectors are input into the graph convolutional network as node features， achieving high-level modeling of category relationship. Finally， the obtained relational features are fused with features extracted by the YOLO11 backbone network through an attention mechanism for subsequent classification and localization branches of the detection head. Experimental results on a self-made infrared aircraft dataset demonstrate that， compared with YOLO11， the proposed algorithm improves mAP50 and mAP50：95 by 1.08% and 0.86%， respectively， with comparable computational complexity and parameter count. Compared with other state-of-the-art algorithms， the proposed algorithm also achieves optimal detection accuracy. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed algorithm in infrared target detection task under complex environments.
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