Infrared and Laser Engineering, Volume. 51, Issue 4, 20220171(2022)
High-dynamic infrared small target detection based on double-neighborhood difference amplification method(Invited)
Figures & Tables(14)
Fig. 1. (a) Infrared image of the dark target and the local 3D gray image of the target; (b) Infrared image of the bright target and the local 3D gray image of the target
Fig. 2. (a) Working mode of sliding window and the division of multi-layer area; (b) A number of subwindows within a sliding window; (c) Schematic diagram of interpolation multiplication in four directions of the algorithm in this paper
Fig. 3. (a) Small size target detection; (b) Large size target detection
Fig. 4. Detection result of multi-scale bright and dark target by DDAM
Fig. 6. Detection results of nine algorithms for three groups of real infrared sequences
Fig. 8. Nine algorithms for TTSM of three sets of real infrared sequences
Fig. 9. ROC curves of nine algorithms under three different scenes
Fig. 10. Detection results of DDAM in nine consecutive frames of complex scenes
Table 1. Three groups of test data parameters
View tableView in ArticleTable 1. Three groups of test data parameters
Sequences Image resolution Image number Target size Target brightness Scenes description 1 400×560 50 2×3-4×6 Dark and bright Complex background interference 2 420×560 90 3×4-6×9 Bright Strong noise interference 3 512×640 115 6×9-9×9 Bright Strong edge interference
Table 2.
\begin{document}$ \overline{\mathrm{S}\mathrm{C}\mathrm{R}\mathrm{G}} $\end{document} \begin{document}$ \overline{\mathrm{B}\mathrm{S}\mathrm{F}} $\end{document} View tableView in ArticleTable 2.
\begin{document}$ \overline{\mathrm{S}\mathrm{C}\mathrm{R}\mathrm{G}} $\end{document} \begin{document}$ \overline{\mathrm{B}\mathrm{S}\mathrm{F}} $\end{document} Evaluation indicators Sequences Top-hat LCM MPCM RLCM TTLCM ADMD DNGM DLCM DDAM $ \overline{\mathrm{S}\mathrm{C}\mathrm{R}\mathrm{G}} $ 1 10.564 NaN 45.296 NaN NaN 83.915 305.688 NInf=1 369.42 NInf=5 342.158 NInf=6 2 5.352 2.225 8.016 2.453 7.915 8.991 159.462 NInf=38 169.824 NInf=40 286.574NInf=38 3 1.939 0.530 11.758 0.908 13.278 14.809 138.146 NInf=26 63.864 NInf=35 62.391 NInf=37 $ \overline{\mathrm{B}\mathrm{S}\mathrm{F}} $ 1 1.206 0.767 NInf=3 4.427 0.721 NInf=1 4.394 NInf=4 13.601 46.914 NInf=1 55.782 NInf=5 54.69 NInf=6 2 1.390 0.358 3.232 0.437 2.250 4.806 73.152 NInf=38 80.489 NInf=40 135.201NInf=38 3 1.384 0.445 6.244 4.055 6.154 10.188 107.484 NInf=26 73.264 NInf=35 83.793 NInf=37 Time/s 1 0.019 0.121 0.135 6.328 4.101 0.034 0.162 0.152 0.120 2 0.015 0.114 0.130 5.613 3.571 0.031 0.158 0.148 0.113 3 0.015 0.163 0.192 7.620 4.799 0.036 0.227 0.215 0.170
Table 3. Detection accuracy of nine algorithms in three groups of stypical cenarios
View tableView in ArticleTable 3. Detection accuracy of nine algorithms in three groups of stypical cenarios
Sequences TPR Top-hat LCM MPCM RLCM TTLCM ADMD DNGM DLCM DDAM 1 Pd-4 0.720 NaN 0.920 NaN NaN 0.720 0.740 0.740 0.960 Pd-3 0.760 NaN 0.960 NaN NaN 0.740 0.780 0.780 0.960 Pd-2 0.820 0.480 0.960 0.780 0.760 0.780 0.780 0.780 0.980 2 Pd-4 0.900 NaN 0.811 NaN 0.844 0.728 0.944 0.967 0.900 Pd-3 0.956 0.678 0.844 0.811 0.922 0.822 0.967 0.967 0.989 Pd-2 0.978 0.864 0.966 0.956 0.989 0.978 0.978 0.973 0.991 3 Pd-4 0.238 0.103 0.276 NaN 0.353 0.036 0.413 0.201 0.370 Pd-3 0.370 0.181 0.785 0.388 0.974 0.931 0.982 0.982 0.982 Pd-2 0.982 0.371 0.982 0.474 0.982 0.982 0.982 0.982 0.982
Table 4. Average performance comparison of several target detection algorithms on twelve scences
View tableView in ArticleTable 4. Average performance comparison of several target detection algorithms on twelve scences
Algorithms Top-hat LCM MPCM RLCM TTLCM ADMD DNGM DLCM DDAM $ \overline{\mathrm{S}\mathrm{C}\mathrm{R}\mathrm{G}} $ 6.251 6.106 20.976 2.473 9.112 37.572 205.098 205.036 230.499 $ \overline{\mathrm{B}\mathrm{S}\mathrm{F}} $ 1.471 0.527 4.977 2.738 4.279 10.138 76.398 69.895 93.388 Time/s 0.016 0.135 0.157 6.523 4.218 0.038 0.184 0.173 0.139 TPR Pd-4 0.609 0.467 0.673 0.499 0.572 0.523 0.703 0.629 0.747 TPR Pd-3 0.667 0.512 0.863 0.653 0.892 0.828 0.905 0.909 0.933 TPR Pd-2 0.910 0.646 0.925 0.749 0.923 0.913 0.926 0.912 0.949
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Shuai Yuan, Xiang Yan, Yugeng Zhang, Hanlin Qin. High-dynamic infrared small target detection based on double-neighborhood difference amplification method(Invited)[J]. Infrared and Laser Engineering, 2022, 51(4): 20220171
Paper Information
Category: Special issue—Infrared detection and recognition technology under superspeed flow field
Received: Jan. 10, 2022
Accepted: Apr. 11, 2022
Published Online: May. 18, 2022
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