基于HVS的空中红外小目标快速检测算法
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摘要
针对传统目标检测算法实时性较差且在面对复杂云层干扰时虚警率高的问题,提出一种基于人类视觉系统的小目标快速检测算法。利用局部对比度测量方法计算候选目标,根据拉普拉斯高斯尺度空间理论,计算候选目标处的多尺度滤波响应,进而通过自适应阈值分割获取真实目标。实验结果表明,该算法的检测率高达97%,虚警率低于3%,且能够在5 ms内完成目标位置计算。
Aiming at the problems that the traditional target detection algorithm has poor real-time performance and high false alarm rate in the face of complex cloud interference,a small target fast detection algorithm based on Human Visual System(HVS) is proposed.The candidate target is calculated using the Local Contrast Measure(LCM) method.According to the Laplace Gaussian scale space theory,the multi-scale filter response at the candidate target is calculated,and then the real target is obtained by adaptive threshold segmentation.Experimental results show that the proposed algorithm has a detection rate of up to 97%,a false alarm rate of less than 3%,and the calculation of the target position can be completed within 5 ms.
Aiming at the problems that the traditional target detection algorithm has poor real-time performance and high false alarm rate in the face of complex cloud interference,a small target fast detection algorithm based on Human Visual System(HVS) is proposed.The candidate target is calculated using the Local Contrast Measure(LCM) method.According to the Laplace Gaussian scale space theory,the multi-scale filter response at the candidate target is calculated,and then the real target is obtained by adaptive threshold segmentation.Experimental results show that the proposed algorithm has a detection rate of up to 97%,a false alarm rate of less than 3%,and the calculation of the target position can be completed within 5 ms.
引文
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[16] 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(3):814-822.
[2] PAN R,REEVES S J.Efficient Huber-Markov edge-preserving image restoration[J].IEEE Transactions on Image Processing,2006,15(12):3728-3735.
[3] SCANLAN L,LEE W,VAHEY M,et al.RASSP methodology evaluation and lessons learned developing IRST signal processor[J].Journal of VLSI Signal Processing,1997,15(1/2):145-160.
[4] HADHOUD M M,THOMAS D W.The two-dimensional adaptive LMS algorithm[J].IEEE Transactions on Circuits and Systems,1988,35(5):485-494.
[5] 刘昆,刘卫东.基于加权融合特征与Ostu分割的红外弱小目标检测算法[J].计算机工程,2017,43(7):253-260.
[6] HU Tun,ZHAO JiaJia,CAO Yuan,et al.Infrared small target detection based on saliency and principle component analysis[J].Journal of Infrared and Millimeter Waves,2010,29(4):303-306.
[7] ZHAO Jiajia,TANG Zhengyuan,YANG Jie,et al.Infrared small target detection using sparse representation[J].Journal of Systems Engineering and Electronics,2011,22(6):897-904.
[8] DAI Yimian,WU Yiquan,SONG Yu,et al.Non-negative infrared patch-image model:robust target-background separation via partial sum minimization of singular values [J].Infrared Physics and Technology,2017,81:182-194.
[9] KIM S,YANG Y,LEE J,et al.Small target detection utilizing robust methods of the human visual system for IRST[J].Journal of Infrared,Millimeter and Terahertz Waves,2009,30(9):994-1011.
[10] SHAO Xiaopeng,FAN Hua,LU Guangxu,et al.An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system[J].Infrared Physicsand Technology,2012,55(5):403-408.
[11] CHEN C L P,LI Hong,WEI Yantao,et al.A local contrast method for small infrared target detection[J].IEEE Transactions on Geoscienceand Remote Sensing,2013,52(1):574-581.
[12] HAN Jinhui,MA Yong,ZHOU Bo,et al.A robust infrared small target detection algorithm based on human visual system[J].IEEE Geoscienceand Remote Sensing Letters,2014,11(12):2168-2172.
[13] CUI Zheng,YANG Jingli,JIANG Shouda,et al.An infrared small target detection algorithm based on high-speed local contrast method[J].Infrared Physics and Technology,2016,76:474-481.
[14] ARDOUIN J P.Point source detection based on point spread function symmetry[J].Optical Engineering,1993,32(9):2156-2164.
[15] 徐文晴,王敏.基于自适应形态学滤波的红外小目标检测算法[J].激光与红外,2017,47(1):108-113.
[16] 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(3):814-822.