基于改进LCM的红外小目标检测算法

Infrared dim target detection algorithm based on improved LCM

如何在复杂背景和低信杂比条件下准确检测到小目标对于精确制导武器的发展和红外预警等具有重要意义。为了在复杂背景条件下提高图像信杂比并有效地检测出小目标,提出一种基于中心域与邻域灰度对比度的红外小目标检测方法。通过计算输入图像的对比度图和显著度图,提高了目标对比度同时抑制背景杂波;在此基础上自适应设定阈值分离出小目标。实验结果表明:与传统LCM(Local Contrast Measure)方法相比,所提出的方法能够取得更高的检测率和较低的虚警率,尤其是对于复杂背景下的弱小目标检测,相对于对比算法,优势更明显。

How to detect infrared dim targets accurately under complex background and low SCR conditionis of great significance for the development of precision guided weapons and infrared warning. In order toimprove the SCR and detect the dim targets effectively, a new method for infrared dim target detection basedon the gray contrast between the central region and its neighborhood was proposed. The contrast of the targetwas improved by calculating the contrast map and saliency map of the input image while suppressing thebackground clutter. The adaptive threshold was set on this basis to separate the dim targets. Experimentalresults show that the proposed method can achieve higher detection rate and lower false alarm rate comparedwith conventional LCM （Local Contrast Measure） method. The proposed method has an outperformancecompared with other algorithms, especially in the case of complex background.