电子科技大学学报2026，Vol.55Issue(1)：100-108,9.DOI:10.12178/1001-0548.2025060
结合图表示学习和多特征融合的红外小目标检测
Infrared small target detection based on multi-feature fusion combined with graph representation learning
摘要
Abstract
Infrared target detection is one of the core technologies in infrared search and tracking systems.In complex backgrounds,infrared target signals are weak and there are numerous irregular sources of interference,which can easily lead to false alarms.To address this issue,this paper proposes an infrared small target detection algorithm that combines graph representation learning and multi-feature fusion.Initially,morphological methods are used to extract candidate target regions.Then,considering irregular false alarm sources and targets are difficult to represent visually in a coordinated manner,the candidate target regions are transform from the image domain to the graph domain to extract both handcrafted features based on images and deep features based on graph representation learning.Finally,a fully connected network is used for feature fusion and classification,thereby filtering out the false alarm regions and obtaining the target regions.The performance comparison experiments are conducted on a public infrared small target dataset,and the results show that the proposed algorithm has good detection performance in complex scenarios.关键词
红外小目标/目标检测/图表示学习/特征融合/深度特征Key words
infrared small target/object detection/graph representation learning/feature fusion/deep features分类
信息技术与安全科学引用本文复制引用
邓佳坤,尹益卓,张彦博,龙畅,李科萱,崔兴晔,彭真明..结合图表示学习和多特征融合的红外小目标检测[J].电子科技大学学报,2026,55(1):100-108,9.基金项目
国家自然科学基金(61571096) （61571096）
四川省自然科学基金(2025ZNSFSC0522) （2025ZNSFSC0522）
