Abstract
To suppress background clutter and improve detection accuracy, we propose a dim target detection algorithm based on density peak search and region consistency. A density peak search algorithm is first applied to extract candidate targets, and these are then classified and marked according to the local mosaic probability factor, which is important in order to suppress the backgroundsssss clutter and accurately strip the candidate target region from the background. Based on the regional stability of the dim targets, local mosaic gradient factors are used to screen real targets from candidates, and a facet kernel filter is used to extract the irregular contours of dim targets with the aim of enhancing them. Our experimental results show that compared with existing algorithms, the proposed method has better detection accuracy and robustness in various complex scenarios.
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Acknowledgements
The authors thank the anonymous reviewers and editors for the very constructive comments. This work was supported by the National Natural Science Foundation of China(61962046,61663036,61841204). Inner Mongolia Outstanding Youth Cultivation Fund (2018JQ02). Inner Mongolia Natural Science Foundation (2015MS0604).
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Zhang, B., Zhu, J., Lu, X. et al. An infrared dim target detection algorithm based on density peak search and region consistency. Opt Quant Electron 53, 396 (2021). https://doi.org/10.1007/s11082-021-03056-x
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DOI: https://doi.org/10.1007/s11082-021-03056-x