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

红外目标检测是红外搜索与跟踪系统的核心技术之一。在复杂背景下红外目标信号微弱，且存在大量不规则干扰源，容易引发虚警。针对这一问题，提出了一种结合图表示学习和多特征融合的红外小目标检测算法。首先采用形态学方法提取目标候选区域；由于不规则虚警源和目标在视觉上难以协同表征，将目标候选区从图像领域转换到图领域，并分别提取基于图像的手工特征和基于图表示学习的深度特征；最后使用全连接网络进行特征融合和分类，筛除虚警区域，得到目标区域。在公开的红外小目标数据集上进行了性能对比实验，结果表明该算法在复杂场景下具有较好的检测性能。

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

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基金资助

国家自然科学基金(61571096)

四川省自然科学基金(2025ZNSFSC0522)