In photoelectric countermeasure systems, the infrared imaging of missiles is critical for automatic recognition and tracking technology of aerial targets. However, complex and newly emerging infrared interference signals severely hinder the recognition performance and lock the target ability of infrared thermal imaging systems. Although considerable progress has been achieved in the development of machine vision systems for missile detection, their performance and robustness should be improved. The brain can detect learned objects in various nonideal situations (partial occlusion and various perspectives). A novel graph network learning framework was developed for object recognition. This brain-inspired anti-interference recognition model can be used for detecting aerial targets composed of various spatial relationships. A spatially correlated skeletal graph model was used to represent the prototype using the graph convolutional network. Furthermore, a novel anti-occlusion framework based on a multisemantic skeleton graph model was proposed to overcome the discontinuity of target features caused by occlusion. In this method, the location of occluded key points was inferred by learning high-order relationships and node topology information. In this study, local image features were considered as graph nodes and a high-order relationship learning module was proposed to transfer relational information between nodes. In this module, the degree of connection between target keypoints was learned to automatically suppress the delivery of meaningless features. Second, a high-order topology learning module that simultaneously learns topological information and embeds local features was proposed to directly predict node similarity scores. Finally, extensive experiments were conducted on the constructed aerial target flight infrared dataset to validate the effectiveness of the proposed model.

中文翻译：

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多语义图骨架模型的抗遮挡红外空中目标识别


在光电对抗系统中，导弹的红外成像对于空中目标的自动识别和跟踪技术至关重要。然而复杂的、新出现的红外干扰信号严重阻碍了红外热成像系统的识别性能和锁定目标的能力。尽管用于导弹探测的机器视觉系统的开发已经取得了相当大的进展，但其性能和鲁棒性还有待提高。大脑可以在各种非理想情况下（部分遮挡和各种视角）检测学习对象。为对象识别开发了一种新颖的图网络学习框架。这种类脑抗干扰识别模型可用于检测由各种空间关系组成的空中目标。使用空间相关的骨架图模型来表示使用图卷积网络的原型。此外，提出了一种基于多语义骨架图模型的新型抗遮挡框架，以克服遮挡引起的目标特征的不连续性。在该方法中，通过学习高阶关系和节点拓扑信息来推断被遮挡关键点的位置。在本研究中，局部图像特征被视为图节点，并提出了高阶关系学习模块来在节点之间传递关系信息。在该模块中，学习了目标关键点之间的连接程度，以自动抑制无意义特征的传递。其次，提出了一种同时学习拓扑信息和嵌入局部特征的高阶拓扑学习模块来直接预测节点相似度得分。 最后，对构建的空中目标飞行红外数据集进行了广泛的实验，以验证所提模型的有效性。