Fast automatic density clustering-based detection method for scale-variable infrared small target

Abstract

The invention relates to a fast automatic density clustering-based detection method for a variable-scale infrared small target and belongs to the image analysis and image understanding field. According to the method, an SURF (speeded-up robust feature) operator is utilized to realize feature extraction of the variable-scale infrared small target; the problem that features are sensitive to the environment is solved; according to a problem domain, a fast automatic density clustering algorithm directly completes trajectory segmentation and extraction from a space domain, and therefore, the problem of exponential complexity caused by data fusion in original sequence detection can be solved; the problems of over-segmentation and under-segmentation in a clustering process can be solved, and the integrity and independence of trajectory extraction and the automatic selection of a clustering center are ensured; In later-stage trajectory extraction, a backtracking algorithm is used to find an optimal solution, the smooth invariant constraint of trajectories is fused into the design of a pruning function, so that unrelated clutter branches can be cut off quickly, and therefore, the speed of solution search can be increased; and the robust feature detection operator and the backtracking strategy are used in combination, and therefore, the detection problem of the variable-scale infrared small target can be solved, and the real-time performance and robustness of the algorithm can be improved.

Description

technical field [0001] The invention relates to image analysis and image comprehension, in particular to a detection method for variable-scale infrared small targets based on fast automatic density clustering. Background technique [0002] A series of problems such as detection, tracking, and recognition of small infrared targets mainly come from the Infrared Search and Track (IRST) system. Its main task is to effectively detect, identify and track interesting targets such as aircraft and ships in complex scenes such as sky and sea. Due to its long distance, the target image in the image plane is generally only a few pixels in size, lacking information such as shape and texture. In the environment of Signal-to-Clutter Ratio (SCR), it is a typical weak target. For a long time, it has been the difficulty and focus of the field of automatic target recognition (Automatic Target Recognition, ATR). [0003] The Track Before Detection (TBD) method based on sequential images becam...

AI Technical Summary

Problems solved by technology

[0008] The main defect of the density clustering algorithm is that it cannot automatically determine the cluster center (it still needs to be manually selected) and cannot overcome two types of errors caused by cluster segmentation: over-segmentation and under-segmentation.

These two kinds of wrong clustering will lead to low detection probability and high false detection rate (False detection)

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