Wang Xiaotian, Lu Ruitao, Bi Haixia, Li Yuhai
Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2023 Oct 20;23(20):8608. doi: 10.3390/s23208608.
The human visual attention system plays an important role in infrared target recognition because it can quickly and accurately recognize infrared small targets and has good scene adaptability. This paper proposes an infrared small target detection method based on an attention mechanism, which consists of three modules: a bottom-up passive attention module, a top-down active attention module, and decision feedback equalization. In the top-down active attention module, given the Gaussian characteristics of infrared small targets, the idea of combining knowledge-experience Gaussian shape features is applied to implement feature extraction, and quaternion cosine transform is performed to achieve multi-dimensional fusion of Gaussian shape features, thereby achieving complementary fusion of multi-dimensional feature information. In the bottom-up passive attention module, considering that the difference in contrast and motion between the target and the background can attract attention easily, an optimal fast local contrast algorithm and improved circular pipeline filtering are adopted to find candidate target regions. Meanwhile, the multi-scale Laplacian of the Gaussian filter is adopted to estimate the optimal size of the infrared small target. The fast local contrast algorithm based on box filter acceleration and structure optimization is employed to extract local contrast features, and candidate target regions can be obtained by using an adaptive threshold. Besides, the mean gray, target size, Gaussian consistency, and circular region constraint are used in pipeline filtering to extract motion regions, and the false-alarm rate is reduced effectively. Finally, decision feedback equalization is adopted to obtain real targets. Experiments are conducted on some real infrared images involving complex backgrounds with sea, sky, and ground clutters, and the experimental results indicate that the proposed method can achieve better detection performance than conventional baseline methods, such as RLCM, ILCM, PQFT, MPCM, and ADMD. Also, mathematical proofs are provided to validate the proposed method.
人类视觉注意力系统在红外目标识别中起着重要作用，因为它能够快速、准确地识别红外小目标，并且具有良好的场景适应性。本文提出了一种基于注意力机制的红外小目标检测方法，该方法由三个模块组成：自底向上的被动注意力模块、自顶向下的主动注意力模块和决策反馈均衡。在自顶向下的主动注意力模块中，鉴于红外小目标的高斯特性，应用结合知识经验高斯形状特征的思想来实现特征提取，并进行四元数余弦变换以实现高斯形状特征的多维度融合，从而实现多维度特征信息的互补融合。在自底向上的被动注意力模块中，考虑到目标与背景之间的对比度和运动差异容易吸引注意力，采用最优快速局部对比度算法和改进的循环管道滤波来寻找候选目标区域。同时，采用高斯滤波器的多尺度拉普拉斯算子来估计红外小目标的最优尺寸。采用基于盒式滤波器加速和结构优化的快速局部对比度算法来提取局部对比度特征，并通过自适应阈值获得候选目标区域。此外，在管道滤波中使用平均灰度、目标尺寸、高斯一致性和圆形区域约束来提取运动区域，有效降低了误报率。最后，采用决策反馈均衡来获得真实目标。在一些包含复杂背景（如海洋、天空和地面杂波）的真实红外图像上进行了实验，实验结果表明，所提出的方法比传统的基线方法（如RLCM、ILCM、PQFT（可能有误，原文未明确，推测是PQFT）、MPCM和ADMD）能实现更好的检测性能。此外，还提供了数学证明来验证所提出的方法。
