复杂天空背景下的红外弱小目标检测算法研究

Research on Weak and Small Infrared Target Detection Algorithm Under Complex Sky Background

为了提高单帧红外图像的检测概率,稳定检测到图像序列中的弱小目标,基于改进的双边滤波与多项式拟合,提出了一种复杂天空背景下的红外弱小目标检测算法。在传统双边滤波算法的权值系数中引入背景相关度因子,有效降低了背景抑制时目标点的影响,提高了目标区域的信噪比以及单帧图像的检测率。为了进一步剔除虚假目标,基于融合目标运动特征,对目标点进行多帧确认。针对序列检测中目标闪烁造成的目标漏检,引入多项式拟合算法对下一帧目标位置进行预测,有效避免了目标轨迹截断的问题。实验结果表明,在信噪比小于2的情况下,该算法能够稳定检测到复杂天空背景下的弱小目标轨迹。

In order to improve the detection probability of a single-frame infrared image and stably detect weak and small targets in image sequences, a weak and small infrared target detection algorithm under complex sky background is proposed based on the improved bilateral filtering and polynomial fitting. The background correlation factor is introduced into the weight coefficient of the traditional bilateral filtering algorithm to effectively reduce the influence of the target point during background suppression, improve the signal-to-noise ratio of the target area and the detection rate of the single frame image. In order to further eliminate the false target, the motion features of the fusion targets are combined to perform multi-frame confirmation on the target point. Aiming at the target miss detection caused by target flicker in sequence detection, the polynomial fitting algorithm is introduced to predict the target position of the next frame, which effectively avoids the problem of truncation of the target trajectory. The experimental results show that the algorithm can stably detect weak and small target trajectories under complex sky backgrounds when the signal-to-noise ratio is less than 2.