Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.

红外（IR）小目标检测是红外搜索与跟踪（IRST）系统的关键技术之一。现有方法在检测性能上存在一定的局限性，尤其是当目标尺寸不规则或背景复杂时。在本文中，我们提出了一种像素级局部对比度测量（PLLCM），它可以同时在像素级细分小目标和背景。通过像素级分割，目标与背景之间的差异变得更加明显，有助于提高检测性能。首先，我们设计了一个多尺度滑动窗口来快速提取候选目标像素。然后，设计了一个基于随机游走器（RW）的局部窗口用于像素级目标分割。在那之后，提出了结合概率权重和尺度约束的PLLCM，以准确测量局部对比度并抑制各种类型的背景干扰。最后，应用自适应阈值操作将目标从 PLLCM 增强映射中分离出来。实验结果表明，与基线算法相比，该方法具有更高的检测率和更低的误报率，同时实现了较高的速度。