Infrared small target detection algorithm based on double neighborhood contrast measure
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摘要: 为了解决密集多目标检测中易造成的漏检问题，提出一种基于双邻域对比度的红外小目标检测算法。首先利用峰值搜索算法筛选出候选目标；再通过单尺度3层双邻域窗口遍历候选目标; 最后利用双邻域对比度模型计算候选目标区域的最小灰度对比度，并用对角梯度因子增强对比度和抑制杂波。结果表明，与5种对比方法相比，该方法的背景抑制因子和对比度增益分别平均提高4.7倍和1.8倍，有效地抑制了杂波，增强了目标。该研究能够准确地检测到相互接近的多个目标，对提高复杂背景下的多目标检测精度是有帮助的。Abstract: In order to solve the problem of missed detection easily caused in dense multi-target detection, an infrared small target detection algorithm based on double neighborhood contrast measure was proposed. First, the peak search algorithm was used to screen out the candidate targets; then the candidate targets were traversed through a single-scale three-layer double neighborhood window; finally the dual-neighbor contrast model was used to calculate the minimum gray contrast of the candidate target area, and the contrast and suppresses clutter were enhanced by the diagonal gradient. The results show that compared with the five comparison methods, the background suppression factor and contrast gain of this method are increased by 4.7 times and 1.8 times on average, respectively, which effectively suppresses clutter and enhances the target. This research can accurately detect multiple targets that are close to each other, which is helpful to improve the accuracy of multi-target detection in complex backgrounds.
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Figure 1. Expansion effect of multi-scale methods
a—original image b—expansion effect diagram
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Figure 4. Frame 1, 2, 3, 4, 5 five original image sequences and detection results under different methods
表 1 BSF, CG and average running time of different algorithms under each image sequence
methods top-hat VARD LCM MPCM RLCM proposed BSF frame 1 3.754 8.718 1.356 5.947 3.562 10.029 frame 2 2.423 9.154 2.450 11.366 2.869 12.263 frame 3 1.648 5.634 1.339 2.514 2.939 9.937 frame 4 9.436 21.198 1.730 7.819 7.820 23.075 frame 5 3.104 2.438 12.480 1.628 4.388 12.751 CG frame 1 2.385 2.483 1.914 1.634 2.396 2.484 frame 2 1.691 1.743 1.507 1.595 1.339 1.758 frame 3 2.473 2.325 1.588 0.479 2.448 2.681 frame 4 0.998 2.131 1.409 1.695 1.515 2.233 frame 5 0.996 1.426 0.954 1.426 0.631 1.431 time frame 1 0.570 0.067 0.116 0.103 1.049 0.336 frame 2 0.380 0.066 0.135 0.118 2.650 0.815 frame 3 0.430 0.069 0.150 0.126 3.553 1.023 frame 4 0.470 0.082 0.167 0.153 4.553 1.200 frame 5 0.732 0.102 0.131 0.076 3.358 0.873 
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