Infrared dim and small target detection has an important role in the infrared thermal imaging seeker, infrared search and tracking system, space-based infrared system and other applications. Inspired by human visual system (HVS), based on the fusion of significant features of targets, the present study proposes an infrared dim and small target detection algorithm for complex backgrounds. Firstly, in order to calculate the target saliency map, the proposed algorithm initially uses the difference of Gaussian (DoG) and the contourlet filters for the preprocessing and fusion, respectively. Then the multi-scale improved local contrast measure (ILCM) method is applied to obtain the interested target area, effectively suppress the background clutter and improve the target signal-to-clutter ratio. Secondly, the optical flow method is used to estimate motion regions in the saliency map, which matches with the interested target region to achieve the initial target screening. Finally, in order to reduce the false alarm rate, forward pipeline filtering and optical flow estimation information are applied to achieve the multi-frame target recognition and achieve continuous detection of dim and small targets in image sequences. Experimental results show that compared with the conventional Tophat (TOP-HAT) and ILCM algorithms, the proposed algorithm can achieve stable, continuous and adaptive target detection for multiple backgrounds. The area under curve (AUC) and the harmonic average measure F1 are used to measure the overall performance and optimal performance of the target detection effect. For sky, sea and ground backgrounds, the test results of proposed algorithm for most sequences are 1. It is concluded that the proposed algorithm significantly improves the detection effect.

红外弱小目标检测在红外热成像寻星仪，红外搜索与跟踪系统，天基红外系统等应用中具有重要作用。受人类视觉系统（HVS）的启发，基于目标的显着特征的融合，本研究提出了一种针对复杂背景的红外弱小目标检测算法。首先，为了计算目标显着性图，该算法首先使用高斯（DoG）和Contourlet滤波​​器的差分别进行预处理和融合。然后，采用多尺度改进局部对比度测量（ILCM）方法获得感兴趣的目标区域，有效抑制背景杂波并提高目标信杂比。其次，光流法用于估计显着图中的运动区域，该运动区域与感兴趣的目标区域匹配以实现初始目标筛选。最后，为了降低误报率，应用前向流水线滤波和光流估计信息来实现多帧目标识别，并连续检测图像序列中的弱小目标。实验结果表明，与传统的Tophat（TOP-HAT）算法和ILCM算法相比，该算法可以在多种背景下实现稳定，连续和自适应的目标检测。曲线下面积（AUC）和谐波平均值测量F1用于测量目标检测效果的整体性能和最佳性能。对于天空，海洋和地面背景，