Method for detecting small targets in infrared sequence images

Abstract

The invention discloses a method for detecting small targets in infrared sequence images, which belongs to the field of image data processing. The method includes the following steps: (1) image preprocessing step: obtaining the differential image of each frame of image of an obtained infrared sequence image; (2) background modeling step: obtaining the characteristic image of each frame of image; (3) smallest non-uniform image segmentation step: working out an optimal segmentation threshold value, and utilizing the optimal segmentation threshold value to segment the obtained characteristic image, so that a small infrared target can be detected. For an image with a uniformly distributed background, the method can effectively rebuild the background; the smallest non-uniform image segmentation method converts the segmentation problem into the segmentation problem of images, the optimal segmentation threshold value is sought from the perspective of segmentation energy, and targets can be more accurately segmented out.

Description

technical field [0001] The invention belongs to an image data processing method, and in particular relates to a detection method of an infrared small target under a complex background. Background technique [0002] Infrared small target can be said to be a relative concept. The so-called "small" refers to its small geometric size in the image. It is generally considered that a small target refers to a target with a size between 1*1 pixel and 10*10 pixels on the image. . Due to the small size of the target, there is no shape information that can be used for identification, and the signal-to-noise ratio is low, and the position and motion speed are unknown, which makes the detection of small infrared targets difficult. The existing infrared small target detection algorithms mainly include detection algorithms based on filters, detection algorithms based on wavelet transform, detection algorithms based on mathematical morphology and some detection algorithms based on learning ...

AI Technical Summary

Problems solved by technology

The filter-based detection algorithm uses filters to filter out possible target areas and suppress most of the background. This type of method is simple and effective, but the effect is not good when the target is small and the signal-to-noise ratio is low, and it cannot be effectively filtered out. Interference; the detection algorithm based on wavelet transform detects by extracting the different characteristics between the target area and the background, but in the case of small targets, wavelet usually cannot effectively extract the characteristics of the target; the method based on mathematical morphology mainly It is the application of top-hat transformation to suppress the background, but it is very sensitive to the selection of structural elements. In addition, it is also very sensitive to the extraction of small targets under low signal-to-noise ratio; the learning-based method is only available when better training samples are obtained. , in order to have a good detection effect, but usually it is very difficult to obtain better training samples

In short, a series of detection algorithms currently exist have certain limitations.

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