周视红外成像搜索系统中的实时目标检测方法

Real-time target detection algorithm of infrared imaging alarm system in panoramic field-of-view

在周视红外成像的预警搜索系统中,大视场下红外图像的背景成分十分复杂;与此同时,高分辨成像使得图像数据量也急剧增加。针对周视成像系统中红外图像的特点,提出了一种基于分块图像加权熵值矩阵的快速目标提取算法:首先根据大视场下红外图像的空间分布特性,对原始图像建立子图像块矩阵;然后提出一种加权熵的特征判别函数,建立子图像块的加权熵值矩阵;最后分析了基于加权熵矩阵自适应阈值选取方法,对背景进行分类并快速提取目标兴趣区。实测数据结果表明:该算法流程是一种适合大视场条件下的有效目标检测算法,且具备良好的工程应用性。

In the panoramic field-of-view （FOV） infrared imaging search system （PIRSS）, the infrared image background was especially complicated, along with rapidly increasing of the data quantity. According to the infrared image′s characteristics in the PIRSS, a flow of detecting algorithm based on the weighted local entropy （WLE） matrix of image blocks was proposed. Firstly, it established the image blocks matrix for the entire image, which was based on the spatial distributing characteristics of the panoramic image. Then a new characteristic function called weighted local entropy was presented, and calculated the WLE matrix for the image blocks. Finally, an appropriate adaptive threshold method based on the analysis of WLE matrix was adopted, which implemented the region separation of candidate targets from background and obtained the ROI. Experimental results demonstrated that the proposed algorithm was effective and befitting for the infrared target detection in large FOV. It also has good performance for real-time processing and engineering realization.