基于改进LCM的红外小目标检测算法

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**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。

Abstract

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

参考文献

[1] Tom V T, Peli T, Leung M, et al. Morphology-based algorithm for point target detection in infrared backgrounds[C]//SPIE, 1993, 1954: 2-11.

[2] 王卫华, 牛照东, 陈曾平. 基于时空域融合滤波的红外运动小目标检测算法[J]. 红外与激光工程, 2005, 34(6): 714-718.

Wang Weihua, Niu Zhaodong, Chen Zengping. Temporal-spatial fusion filtering algorithm for small infrared moving target detection[J]. Infrared and Laser Engineering, 2005, 34(6): 714-718. (in Chinese)

[3] Wang X, Lv G, Xu L. Infrared dim target detection based on visual attention[J]. Infrared Physics & Technoolgy, 2012, 55(6): 513-521.

[4] Qi S, Ma J, Tao C, et al. A robust directional saliency-based method for infrared small-target detection under various complex backgrounds[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 495-499.

[5] Chen C L P, Li H, Wei Y, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581.

[6] 刘运龙, 薛雨丽, 袁素真, 等. 基于局部均值的红外小目标检测算法[J]. 红外与激光工程, 2013, 42(3): 815-822.

Liu Yunlong, Xue Yuli, Yuan Suzhen, et al. Infrared small targets detection using local mean[J]. Infrared and Laser Engineering, 2013, 42(3): 815-822. (in Chinese)

[7] 黄敏, 鲍苏苏, 邱文超. 基于可见光下双目视觉的手术导航研究与仿真[J]. 机器人, 2014, 36(4): 461-468, 476.

Huang Min, Bao Susu, Qiu Wenchao. Study and simulation of surgical navigation based on binocular vision under visible light[J]. Robot, 2014, 36(4): 461-468, 476. (in Chinese)

[8] 宋新, 罗军, 王鲁平, 等. 基于 GVF Snake 的运动目标跟踪方法[J]. 红外与激光工程, 2007, 36(2): 226-228.

Song Xin, Luo Jun, Wang Luping, et al. Motion target tracking based on GVF Snake[J]. Infrared and Laser Engineering, 2007, 36(2): 226-228. (in Chinese)

[9] 孙伟, 王宏飞, 邵锡军. 基于改进分水岭算法的红外图像分割[J]. 红外与激光工程, 2006, 35(S4): 31-37.

Sun Wei, Wang Hongfei, Shao Xijun. Infrared target segmentation method based on improved watershed algorithms[J]. Infrared and Laser Engineering, 2006, 35 (S4): 31-37. (in Chinese)

[10] 杨一帆, 田雁, 杨帆, 等. 基于改进 Mean-Shift 算法的红外小目标跟踪[J]. 红外与激光工程, 2014, 43(7): 2164-2169.

Yang Yifan, Tian Yan, Yang Fan, et al. Tracking of infrared small-target based on improved Mean-Shift algorithm[J]. Infrared and Laser Engineering, 2014, 43(7): 2164-2169. (in Chinese)

[11] 卢瑞涛, 黄新生, 徐婉莹. 基于Contourlet变换和Facet模型的红外小目标检测方法[J]. 红外与激光工程, 2013, 42(8): 2281-2287.

Lu Ruitao, Huang Xinsheng, Xu Wanying. Method of infrared small target detection based on Contourlet transform and Facet model[J]. Infrared and Laser Engineering, 2013, 42(8): 2281-2287. (in Chinese)

张祥越, 丁庆海, 罗海波, 惠斌, 常铮, 张俊超. 基于改进LCM的红外小目标检测算法[J]. 红外与激光工程, 2017, 46(7): 0726002. Zhang Xiangyue, Ding Qinghai, Luo Haibo, Hui Bin, Chang Zheng, Zhang Junchao. Infrared dim target detection algorithm based on improved LCM[J]. Infrared and Laser Engineering, 2017, 46(7): 0726002.

基于改进LCM的红外小目标检测算法

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