一种基于显著性的红外弱小目标检测方法

红外弱小目标检测是目标识别等领域的研究热点。考虑到红外弱小图像中目标信噪比较低，且成像目标的尺度变化较大，构建一种同时考虑局部显著性特征和全局显著性特征的红外弱小目标检测框架。构建一种基于多尺度卷积核的显著性目标检测算法，将该算法与谱残差算法分别进行显著图计算；在得到局部和全局显著图后，采用形态学方法进行显著图的融合以及自适应阈值方法进行二值分割。在给定的公开数据集上的实验结果表明，该方法相对于基准的显著性算法，在目标检测的准确性和虚警率上均有明显优势。

An Infrared Dim and Small Target Detection Method Based on Saliency

Infrared dim and small target detection is a hot research topic in the field of target recognition. Considering the low signal-to-noise ratio of targets in infrared dim and small images and the large scale variation of imaging targets, constructs an infrared dim and small target detection framework considering both local salient features and global salient features. Constructs a saliency detection algorithm based on multi-scale convolution kernel, and calculates the saliency map of the algorithm and the spectral residual algorithm respectively; after obtaining the local and global saliency map, this paper uses the morphological method to fuse the saliency map, and then uses the adaptive threshold method to perform binary segmentation. Experimental results on a given public data set show that the proposed method has obvious advantages over the benchmark saliency algorithm in terms of target detection accuracy and false alarm rate.