Long-distance unmanned aerial vehicle small target detection method based on infrared image

Abstract

The invention provides a long-distance unmanned aerial vehicle small target detection method and system based on an infrared image. The method comprises the following steps: firstly, constructing cumulative directional derivative convolution kernels of multiple scales in each direction of the infrared image; then optimizing the output of the average absolute gray difference algorithm, then calculating the weighting coefficient of the optimized average absolute gray difference algorithm for each scale by adopting a cumulative directional derivative convolution kernel to obtain the weighting coefficient of each direction on each scale, and taking a response with a minimum weighting coefficient in each direction on each scale as a response diagram of the scale; secondly, taking the response diagram on each scale as a weighting coefficient of an optimized average absolute gray difference algorithm to obtain response diagrams on different scales; and finally, taking a maximum response diagram on different scales as a final response diagram, and carrying out contour extraction on the final response graph to obtain a suspected target on the infrared image. According to the method, background clutters are effectively suppressed, the target area is enhanced, and accurate detection of the target is realized.

Description

technical field [0001] The invention relates to the field of unmanned aerial vehicle detection, in particular to a long-distance unmanned aerial vehicle small target detection method based on infrared images. Background technique [0002] Infrared Target Search and Tracking System (IRST) is still difficult to detect long-distance small targets in infrared images due to the characteristics of concealed operation and anti-electronic interference due to low signal-to-noise ratio and complex background environment interference. Detection research can be divided into two categories. The first category is based on background removal methods. In the first category, desired operators (such as maximum median value, morphological opening operation, principal component tracking) are used to learn approximate background. Then, the approximate background is subtracted from the original image. Finally, an appropriate threshold segmentation strategy is used to extract objects. Although t...

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Problems solved by technology

Although this method is straightforward and has low computational complexity, it has a high false alarm rate for complex backgrounds.

And the camera can only point to a fixed area, cannot scan the learning background, and the detection range is limited

[0003] The second type of method is to directly enhance the target area and suppress the background area in the process of infrared small target detection. Considering the Gaussian model of the spatial distribution of small targets, the normalized Laplacian of Gaussian (LoG) is used in the scale space to solve the continuous problem. The change of the target size in the infrared image, although this method can enhance the low signal-to-noise ratio (psnr) target, it is sensitive to background noise due to the use of the second derivative

[0004] However, since the mean absolute gray scale difference algorithm has no difference between positive and negative contrast, the target area of ​​local contrast and the cavity area of ​​local contrast are both negative areas will be strengthened, such as figure 2 As shown, the area corresponding to the hole-shaped area is wrongly enhanced, which in turn increases the false alarm rate. At the same time, when the background of the infrared image contains high-intensity sharp edges, the detection performance of the AAGD algorithm drops sharply, as shown in image 3 shown

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