Point target detection method and system based on machine learning

Abstract

The invention discloses a point target detection method and system based on machine learning, and the method comprises the steps: determining a to-be-detected image, and dividing the to-be-detected image into a plurality of sub-images with preset sizes; filtering each subimage by adopting an MMF to obtain filtering output of each subimage, if the filtering output of each subimage is smaller than or equal to a preset threshold value, judging that the subimage is a background, otherwise, marking the subimage as a candidate subimage, and determining a filtering score of each candidate subimage; obtaining a confidence coefficient score of a target contained in each candidate sub-image through a pre-trained BP neural network classifier; and inputting the filtering score of each candidate sub-image and the confidence score of the target contained in the candidate sub-image into a Logistic regression classifier trained in advance, and judging whether each candidate sub-image contains the target or not by combining an output value of the Logistic regression classifier. The result of the MMF algorithm and the result of the BP algorithm are fused by using the Logistic regression algorithm, so that the classification result is more accurate.

Description

technical field [0001] The present invention relates to the field of image processing and pattern recognition, and more specifically, to a machine learning-based point target detection method and system. Background technique [0002] Point object detection is a key technology in modern surveillance systems. Traditional detection methods are mainly filter-based. Filter-based algorithms regard point targets as high-frequency signals and construct specific filters to eliminate or highlight them. However, filter-based algorithms have poor anti-jamming ability, because the edges of noise and background are also high-frequency signals, and they are always misdetected as targets. Furthermore, most filter-based algorithms require prior information about the target, and they are sensitive to parameters. [0003] With the development of machine learning technology, some new point target detection algorithms have emerged. Y.Bi et al. (Y.Bi, X.Bai, Multiple feature analysis for infra...

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Problems solved by technology

[0005] Aiming at the defects of the prior art, the purpose of the present invention is to solve the problem that the existing point target detection method has poor anti-interference ability and is sensitive to parameters when it is based on a filter; when using SVM classification, the detection result is greatly affected by the threshold; the infrared small target image is regarded as sparse Complicated technical issues in the calculation process when the low-rank background matrix is ​​destroyed by the target and noise matrices

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