基于萤火虫最优偏差的红外目标检测算法

为了提高红外目标检测的性能,借鉴萤火虫算法,提出了一种基于萤火虫最优偏差的红外目标检测算法。算法首先建立红外目标检测模型,通过红外拍摄系统对目标像点定位,将检测目标的光强分布看成高斯分布函数,利用质心窗口采集图像；以红外点目标成像的艾里斑能量分布做为萤火虫适应度函数,利用差分迭代对红外目标进行检测寻优；通过控制最优偏差估计的检测误差来优化检测目标。最后实验仿真显示本文算法能够检测出红外目标区域,相比其他算法边缘定位更准确,同时检测效率较高。

Infrared target detection algorithm based on optimal deviation of firefly method

order to improve the detection performance of infrared target,a new algorithm of infrared target detection based on optimal deviation of firefly method is proposed.Firstly,the infrared target detection model was established,and the target image points were located by infrared camera system,and the intensity distribution of the detected object was regarded as the Gauss distribution function,the algorithm uses the centroid window to collect the image.The Airy spot energy distribution in infrared point target imaging was used as the fitness function of firefly method,and the difference iteration was used to detect the infrared target.The detection targets were optimized by controlling detection error of optimal deviation estimation.Finally,the simulation results show that the algorithm can effectively detect the infrared target area,which is more accurate than other algorithms,and the detection efficiency is higher.