为提高复杂环境下红外小目标的检测率, 提出了基于二阶方向导数极大值的红外小目标检测算法。该算法首先对二阶方向导数的性质进行了分析, 对极大值进行阈值翻转操作, 将背景中的平坦成分和边缘成分剔除。接着, 根据小面模型对背景进行预测, 并以预测误差为权值进一步增强小目标区域。以上2个步骤的计算可通过4个卷积实现, 加快了检测速度。最后, 对少量候选小目标计算局部对比度, 降低了虚警率。实验结果表明: 该检测算法在6种复杂背景下平均信杂比增益为78.413 0, 平均背景抑制因子为35.079 6, 具有较强的鲁棒性和较高的检测率。

In order to improve the detection rate of infrared small-target in complex environment, a infrared small-target detection algorithm based on the maximum of second-order directional derivative was proposed. Firstly, the properties of second-order directional derivative were analyzed, meanwhile, the flat component and edge of background were removed by threshold and flip operations of the maximum. Then, the background was predicted based on facet model and further enhanced the small-target by prediction error as weight. The above two steps can be achieved by four convolutions and the detection speed was accelerated. At last, the local contrast of candidate small-targets was calculated to reduce the false alarm rate. The experimental results show that the average signal to clutter ratio gain is 78.413 0 and the average background suppression factor is 35.079 6 in 6 kinds of complex background. The proposed detection algorithm has stronger robustness and higher detection rate.