一种基于知识模型的红外目标检测方法  

An Infrared Target Detection Algorithm Based on Knowledge Model

作　　者：苏娟[1] 杨罗 卢俊 

机构地区：[1]第二炮兵工程大学,西安710025

出　　处：《光子学报》2013年第7期839-844,共6页Acta Photonica Sinica

基　　金：国家自然科学基金(Nos.61072141;61132008)资助

摘　　要：针对电厂冷却塔这类具有特殊建筑规范的典型目标,在分析目标红外特性与形状特性的基础上,提出了一种基于知识模型的红外目标检测方法.首先根据冷却塔目标的红外特性,提取图像的亮度、方向和局部熵特征,采用视觉注意模型提取红外图像中的显著区域,作为待检测目标的感兴趣区域;在此基础上,根据冷却塔目标的形状特性建立双曲线形状模型,在感兴趣区内进行结构特征边缘提取和形状模型拟合,构建相关判定准则检测出目标.在一组机载前视红外图像上的实验结果表明,该方法可以达到98.67%的查全率和93.97%的查准率,具有较好的目标检测效果.由于本文方法不需要基准图的参与,降低了对数据保障的要求,因此具有较大的实用性.Based on analyzing the infrared characteristics and shape characteristics of condensing tower that has special construction rules, an infrared target detection algorithm based on knowledge model is proposed. Firstly, based on the infrared characteristics of condensing tower, the intensity, orientation and local entropy features are extracted to construct the visual attention model, which is used to extract salient regions as regions of interest in the infrared image. Secondly, based on the shape characteristics of condensing tower, hyperbola shape model is constructed for condensing tower, structure feature edges are extracted in the salient regions and used to fit the hyperbola shape model, and relevant decision rules are constructed to confirm the targets. The recall and precision of the experiment on a set of air-born infrared images can reach up to 98. 670//00 and 93. 670//00 respectively, which demonstrates the excellent performance of the proposed algorithm. Moreover, since the reference image is unnecessary in the proposed algorithm, the requirements for the data preparation is reduced greatly, which improve the practicality of the algorithm.

关 键 词：目标检测 视觉注意模型 显著区域 知识模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]