结合帧差的核相关滤波弱小红外目标检测

Weak and Small Infrared Target Detection Combined With Frame Difference Kernel Correlation Filtering

为了提高红外目标检测的性能,提出了一种结合帧差的核相关滤波弱小红外目标检测算法。算法首先通过核相关滤波训练当前帧获得最大回归值,相对间隔帧求取差值,以此进行循环移位,从而实现对帧间背景运动的补偿;再者借助帧间差分法提取当前帧相对运动特征,增强区分弱小目标和红外背景的能力;最后对相对运动特征进行阈值分割获得最终检测结果。仿真实验显示本算法能有效检测出复杂环境下红外弱小目标,与其他同类算法相比,本算法可以很好地对杂波和点状干扰源进行抑制,获得较高的目标检测率,同时将大量运算置于频域中,运算效率也优于其他算法。

To improve the performance of infrared target detection,weak and small infrared target detection combined with frame difference kernel correlation filtering is proposed.First,the current frame is trained by kernel correlation filtering to obtain the maximum regression value.Then,the difference value is calculated relative to the interval frame to perform a cyclic shift to compensate for the background motion between frames.The relative motion features of the current frame are extracted using the interframe difference method,which enhances the ability to distinguish weak and small targets from the infrared background.Finally,threshold segmentation is performed on the relative motion features to obtain the final detection results.Simulation experiments show that the proposed algorithm effectively detected weak and small infrared targets in complex environments.Compared with similar algorithms,the proposed algorithm suppressed clutter and point-shaped interference sources,and achieved a higher target detection rate.Simultaneously,a large number of operations are placed in the frequency domain,and the operational efficiency is better than that of other algorithms.