To address the issues of unknown target size, blurred edges, background interference and low contrast in infrared small target detection, this paper proposes a method based on density peaks searching and weighted multi-feature local difference. Firstly, an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference, thereby increasing the probability of capturing real targets in the density peak search. Secondly, a triple-layer window is used to extract features from the area surrounding candidate targets, addressing the uncertainty of small target sizes. By calculating multi-feature local differences between the triple-layer windows, the problems of blurred target edges and low contrast are resolved. To balance the contribution of different features, intra-class distance is used to calculate weights, achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets. The real targets are then extracted using the interquartile range. Experiments on datasets such as SIRST and IRSTD-1K show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.
| [1] |
Han J, Wei Y, Peng Z, et al. . Infrared dim and small target detection: a review [J]. Infrared and laser engineering. 2022, 51(4): 20210393(in Chinese)
|
| [2] |
Ren X, Wang J, Ma T, et al. . Review on infrared dim and small target detection technology[J]. Journal of Zhengzhou University (natural science edition). 2020, 52(2): 1-21(in Chinese)
|
| [3] |
Chen C L P, Li H, Wei Y, et al. . A local contrast method for small infrared target detection[J]. IEEE transactions on geoscience & remote sensing. 2014, 52(1): 574-581
|
| [4] |
Han J, Ma Y, Zhou B, et al. . A robust infrared small target detection algorithm based on human visual system[J]. IEEE geoscience & remote sensing letters. 2014, 11(12): 2168-2172
|
| [5] |
Wang X, Peng Z, Zhang P, et al. . Infrared small dim target detection based on local contrast combined with region saliency[J]. High power laser and particle beams. 2015, 9: 091005(in Chinese)
|
| [6] |
Han J, Liang K, Zhou B, et al. . Infrared small target detection utilizing the multiscale relative local contrast measure[J]. IEEE geoscience and remote sensing letters. 2018, 15(4): 612-616
|
| [7] |
He S, Xie Y, Yang Z. High-boost-based local Weber contrast method for infrared small target detection[J]. Remote sensing letters. 2023, 14(2): 103-113
|
| [8] |
Huang S, Peng Z, Wang Z, et al. . Infrared small target detection by density peaks searching and maxi- mum-gray region growing[J]. IEEE geoscience and remote sensing letters. 2019, 16(12): 1919-1923
|
| [9] |
Li W, Wang Q, Gao S. A review of infrared small target detection algorithms based on deep learning[J]. Laser & infrared. 2023, 53(10): 1476-1484(in Chinese)
|
| [10] |
Han J, Liu C, Liu Y, et al. . Infrared small target detection utilizing the enhanced closest-mean background estimation[J]. IEEE journal of selected topics in applied earth observations and remote sensing. 2021, 14: 645-662
|
| [11] |
Wu L, Fang S, Ma Y, et al. . Infrared small target detection based on gray intensity descent and local gradient watershed[J]. Infrared physics & technology. 2022, 123: 104171
|
| [12] |
Lee I, Park C. Infrared small target detection algorithm using an augmented intensity and density-based clustering[J]. IEEE transactions on geoscience and remote sensing. 2023, 61: 1-14
|
| [13] |
Liu Y, Liu X, Hao X, et al. . Single-frame infrared small target detection by high local variance, low-rank and sparse decomposition[J]. IEEE transactions on geoscience and remote sensing. 2023, 61: 1-17
|
| [14] |
Dai Y, Li X, Zhou F, et al. . One-stage cascade refinement networks for infrared small target detection[J]. IEEE transactions on geoscience and remote sensing. 2023, 61: 1-17
|
| [15] |
Zhang M, Zhang R, Yang Y, et al. . ISNet: shape matters for infrared small target detection [C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19–24, 2022, New Orleans, LA, USA. 2022, New York, IEEE: 867876
|
| [16] |
Pan S, Zhang S, Zhao M, et al. . Infrared small target detection based on double-layer local contrast measure[J]. Acta photonica. 2020, 49(1): 0110003 in Chinese)
|
| [17] |
Guan X, Peng Z, Huang S, et al. . Gaussian scale-space enhanced local contrast measure for small infrared target detection[J]. IEEE geoscience and remote sensing letters. 2020, 17(2): 327-331
|
| [18] |
Zhang L, Peng Z. Infrared small target detection based on partial sum of the tensor nuclear norm[J]. Remote sensing. 2019, 11(4): 382
|
| [19] |
Wu F, Yu H, Liu A, et al. . Infrared small target detection using spatiotemporal 4-D tensor train and ring unfolding[J]. IEEE transactions on geoscience and remote sensing. 2023, 61: 1-22
|
| [20] |
Hadi S, Saed M, Shokoufeh A. A noise-robust method for infrared small target detection[J]. Signal, image and video processing. 2023, 17(5): 2489-2497
|
| [21] |
Tang Y, Xiong K, Wang C. Fast Infrared small target detection based on global contrast measure using dilate operation[J]. IEEE geoscience and remote sensing letters. 2023, 20: 1-5
|
| [22] |
Zhao E, Zheng W, Li M, et al. . Infrared small target detection using local component uncertainty measure with consistency assessment[J]. IEEE geoscience and remote sensing letters. 2022, 19: 1-5
|
| [23] |
WAN M, XU Y, HUANG Q, et al. Single frame infrared small target detection based on local gradient and directional curvature[J]. Proc. SPIE, 2021: 11897.
|
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