一种改进GMM-MRF模型的海上红外目标检测

目前海上目标检测已在民用和军事领域得到广泛的应用。由于海水波动大、目标成像面积少、特征不显著等特点增大了目标检测难度,为了消除上述的问题,首先提出了一种基于混合高斯-马尔科夫随机场目标检测模型,该模型利用所提出的混合高斯模型自适应调节学习率来抑制动态背景的干扰。然后,利用混合高斯模型对红外图像所计算的结果作为马尔科夫随机场的观测值,建立了马尔科夫随机场模型,可以解决混合高斯模型存在的不足。实验结果表明,本文的方法能够取得良好的检测效果。

An Improved GMM-MRF Model for Maritime Infrared Target Detection

Currently,maritime target detection has been widely used in the civil and military fields.However,the difficulty of target detection is increased due to the large fluctuations of seawater,the small imaging area of the target,and the insignificant features.To eliminate these problems,a Gaussian mixed-Markov random field target detection model is proposed,which uses the Gaussian mixed model to adjust the learning rate and to suppress the interference of the dynamic background.Then,using the result of the Gaussian mixed model to calculate the infrared image as the observed value of the Markov random field,a Markov random field model is established.The Markov random field model can solve the existing defects of the Gaussian mixed model.The experimental results show that the method in this paper can achieve good detection results.