基于多模态特征图融合的红外热图像目标区域提取算法

Infrared thermal image ROI extraction algorithm based on fusion of multi-modal feature maps

红外热图像目标区域(Region of Interest,ROI)提取对故障检测、目标跟踪等有着重要意义.为解决红外热图像干扰多、需人工标记及准确率低等问题,提出一种基于多模态特征图融合的红外热图像ROI提取算法.通过对比度、熵及梯度特征构建多模态特征图并进行区域填充,实现ROI提取.将新算法应用于实际采集的光伏太阳能板图像中.结果表明,新算法具有平均查准率高(93. 0553%)、平均查全率高(90. 2841%)、F1指数和J指数均优于图割法,人工标记少等优点,可有效用于红外热图像ROI提取.

Infrared thermal image region of interest(ROI)extraction has important significance for fault detection,target tracking and so on.In order to solve the problems of many infrared thermal image disturbances,artificial markers and low accuracy,a ROI of infrared thermal image extraction algorithm based on fusion of multi-modal feature map is proposed.Multi-modal feature maps are constructed by contrast,entropy,and gradient features,and region filling is performed to achieve ROI extraction.New algorithm is applied to actual collected photovoltaic solar panel image.Simulation results show that the proposed algorithm has high average precision(93.055 3%),high average recall(90.284 1%),F 1 index and J index are better than Grab Cut,less artificial marks,etc.It can be effectively used for ROI extraction of infrared thermal images.