弱小点目标检测是红外探测技术中的一个关键问题。针对目前序列红外图像目标检测中单阈值分割时弱小目标易丢失及快速移动目标的能量欠积累问题，提出了一种基于多阈值分类与逆向求证的弱小红外目标检测方法。在背景抑制的基础上，首先采用自适应多阈值分类的方法提取多类候选目标，强化了各类弱小候选目标的检测能力。在当前帧候选目标点的真伪无法判定时，根据目标在相邻帧间的位置变化信息构造相应的时空管道，沿时空管道逆向寻找可能出现的各类候选目标，并将其能量与当前帧候选目标点的能量进行加权求和后再进行门限判决，较好地解决了弱小目标及快速移动目标的能量积累问题。最后，通过若干实际红外数据验证了上述方法的有效性。

Weak and small target detection is a crucial problem in infrared technology Facing the problems of losing weak/small target in single threshold segmentation and insufficient energy accumulation for fast-moving targets in the target detection of infrared image sequence, a new method based on multi-threshold classification and backward verification has been proposed in this paper After background suppression, an adaptive multi-threshold classification is adopted for the extraction of multi-class candidates, which has enhanced the capability of weak and small targets extraction When the candidate in the current frame can not be justified due to its weakness and movement, a kind of spatial-temporal pipeline will be constructed according to the candidate’s movement in adjacent frames, and a backward search will be carried out for possible existence of any kinds of candidate After that, the energies of these candidates will be weighted accumulated with that of the candidate in the current frame and justified with a fixed energy threshold In the end of this paper, some experimental and comparison results using real infrared image sequences will be given to show the effectiveness of this new method