A Dim Object Detection Method Based on Morphological Filtering and SVD

Abstract

The invention discloses a morphological filtering and SVD(singular value decomposition)-based weak target detection method. The method includes the following steps that: step 1, background suppression and noise removal are carried out through a morphological filtering target enhancement algorithm, so that pre-processed image sequences can be obtained; step 2, an image sequence composed of Nmax images is read, and the number of frames is estimated, and the number N of frames requiring processing is obtained; step 3, an image formed by N+1 images is merged to two-dimensional data, and the autocorrelation matrix of the two-dimensional data is solved, and SVD is performed on the autocorrelation matrix; step 4, proper feature vectors are selected to reconstruct an image sequence, so that a new feature image sequence can be obtained; step 5, threshold value segmentation is performed on the reconstructed image sequence, and the position of a weak target in an original image can be separated from background; step 6, each image in the sequence is modified; and step 7, after Nmax is replaced by N, the step 2 to the step 7 are repeated. According to the morphological filtering and SVD-based weak target detection method, a morphological filtering method and a singular value decomposition method are effectively combined to detect weak targets in videos, and therefore, the morphological filtering and SVD-based weak target detection method has the advantages of short computation time, high detection efficiency as well as high accuracy and robustness.

Description

technical field [0001] The method belongs to the field of video analysis, and specifically relates to a small target detection method based on morphological filtering and SVD. Background technique [0002] The detection of small and weak targets is self-evident in modern warfare, and it has become the core technology of information processing in the fields of satellite remote sensing, high-energy physics, low-altitude early warning, and precision guidance. Due to the small number of pixels and the lack of target structure information, there is very little information available for segmentation and detection algorithms. However, the intensity of the target accepted by the sensor is weak, and the interference of noise and background clutter is strong, which reduces the signal-to-noise ratio of the image. Therefore, we should make good use of the continuity and regularity of the target in the sequence image to detect the target. For a long time, how to better use the inter-fra...

AI Technical Summary

Problems solved by technology

However, in a complex background, the target point is easily overwhelmed by noise, and it is difficult to achieve reliable detection and recognition of the target

In addition, it is difficult to meet good detection performance under the conditions of large data throughput and high real-time requirements

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