基于滑窗的自适应红外小目标检测方法

Adaptive infrared small target detection method based on sliding window

为解决传统自适应红外小目标检测方法虚警严重的问题,提出一种滑窗式自适应红外小目标检测方法。结合教学优化算法来增强哈里斯鹰优化算法跳出局部极值的能力,提出一种混合的多种群哈里斯鹰优化算法;设计了基于滑动窗口的小目标搜索适应度函数;提出了红外小目标的自适应检测方法。基于公开数据集的实验结果表明,相较于其它自适应红外小目标检测方法,该检测方法的虚警率较低。

To resolve the problem of the high false alarm rate of traditional adaptive target detection methods of infrared night vision, an adaptive small target detection method of infrared night vision based on sliding window is proposed. First of all, the Harris hawk optimization algorithm is enhanced with combination of teaching learning based optimization algorithm, in order to enhance its ability of escaping from local optimal, a mixed multiple population Harris hawk optimization algorithm is designed;Secondly, a fitness function of small target based on sliding window is designed;Finally, the infrared night vision small target is detected adaptively. Experimental results based on public datasets suggest that, compared to the other adaptive infrared small target detection method, the proposed method has lower false alarm rate for small target detection.