High-dynamic infrared small target detection based on double-neighborhood difference amplification method(Invited)

Shuai Yuan; Xiang Yan; Yugeng Zhang; Hanlin Qin

doi:10.3788/IRLA20220171

Journals >Infrared and Laser Engineering >Volume 51 >Issue 4 >Page 20220171 > Article

Infrared and Laser Engineering
Vol. 51, Issue 4, 20220171 (2022)

High-dynamic infrared small target detection based on double-neighborhood difference amplification method(Invited)

Shuai Yuan, Xiang Yan, Yugeng Zhang, and Hanlin Qin

Author Affiliations

School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China

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DOI: 10.3788/IRLA20220171 Cite this Article

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 Copy Citation Text

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(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. 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

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(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. 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

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Fig. 3. (a) Small size target detection; (b) Large size target detection

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Fig. 4. Detection result of multi-scale bright and dark target by DDAM

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Fig. 5. Flow chart of small object detection

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Fig. 6. Detection results of nine algorithms for three groups of real infrared sequences

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Fig. 7. TTSM construction diagram

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Fig. 8. Nine algorithms for TTSM of three sets of real infrared sequences

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Fig. 9. ROC curves of nine algorithms under three different scenes

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Fig. 10. Detection results of DDAM in nine consecutive frames of complex scenes

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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 1. Three groups of test data parameters

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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 2.

Unknown environment 'document'

Unknown environment 'document'

and real time performance of nine algorithms in three groups of scenarios

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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 3. Detection accuracy of nine algorithms in three groups of stypical cenarios

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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

Table 4. Average performance comparison of several target detection algorithms on twelve scences

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