Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China.
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.
Sensors (Basel). 2022 Jul 8;22(14):5136. doi: 10.3390/s22145136.
Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.
稳健的红外慢速小目标检测在红外搜索和跟踪（IRST）应用中至关重要，例如红外制导和低空安全；然而，现有的方法在检测复杂低空场景中的红外小目标时容易造成漏检和虚警。本文提出了一种新的基于时空特征度量（STFM）的低空慢速小目标检测算法。首先，我们构建一个圆形核来计算单幅图像中的局部灰度差（LGD），这对于抑制空间域中的低频背景和不规则边缘至关重要。然后，提出了一种短期能量聚合（SEA）机制，通过在多个连续帧中积累运动目标能量来增强暗目标。接下来，通过整合这两个操作获得时空显著图（STSM），并从 STSM 中使用自适应阈值机制分割候选目标。最后，设计了一个长期轨迹连续性（LTC）测量来确认真实目标并进一步消除虚警。SEA 和 LTC 模块分别利用了运动小目标在时域中的局部不一致性和轨迹连续性。在包含不同低空场景的六个红外图像序列上的实验结果表明，该方法具有有效性，优于现有的最先进方法。
