场景抽象语义综合模型及其在红外弱小目标检测中的应用

doi:10.7527/S1000-6893.2024.30702

Abstract

Abstract:

Accurate and stable detection of dim and small targets in complex， multi-task， and cross-domain environments plays a critical role in infrared early warning， search and tracking， and precision guidance. Current algorithms overly rely on manually designed strategies and prior knowledge， focusing primarily on target information extraction while insufficiently mining and utilizing scene information. This results in limited performance and inadequate environmental adaptability. To address these issues， this paper proposes a dim and small target detection method based on scene abstract semantic synthesis. First， four scene abstract semantic synthesis models are designed using cross-attention， Siamese networks， extended semantic graphs， and self-learning dual-channel methods. Next， based on these four semantic synthesis models， a dim and small target detection network based on scene semantic synthesis is designed. This network incorporates scene category semantic information into the infrared dim and small target detection process to achieve detection in various complex backgrounds. Finally， experimental results show that the proposed self-learning dual-channel semantic synthesis-based dim and small target detection algorithm achieves an accuracy of 84.24% and a recall rate of 89.68%.

Key words: scene semantics, semantic synthesis, image processing, deep learning, infrared dim and small target detection

CLC Number:

Shaoyi LI, Yaqi ZHANG, Yue CHENG, Xi YANG, Liang ZHANG, Jian LIN, Zhongjie MENG. Scene abstract semantic synthesis model and its application in infrared dim and small target detection[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 630702.

Figures/Tables 24

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Table 1

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Table 2

Quantitative experimental results of infrared dim and small target detection

算法名称	$P r e c i s i o n$ /%	$R e c a l l$ /%	$I o U$ /%	$F 1$ /%	$F P S$
MAXMEAN^［31］	0.014	10.18	0.014	0.029	2.64
AAGD^［1］	11.87	4.02	3.09	6.00	2.53
ADMD^［2］	12.40	6.77	4.58	8.76	2.49
ILCM^［4］	0.14	93.25	0.14	0.29	2.71
MPCM^［32］	0.009 7	9.19	0.009 7	0.019	2.29
RLCM^［33］	7.71	54.76	7.25	13.51	0.68
TLLCM^［34］	17.36	8.09	5.84	11.04	1.49
HB-MLCM^［35］	16.22	8.74	6.02	11.36	2.49
MLCM-LEF^［36］	24.04	8.82	6.09	12.90	0.82
WSLCM^［37］	27.63	10.22	8.06	14.92	0.56
Top-Hat^［38］	1.85	11.06	1.61	3.17	2.78
LIG^［39］	6.13	7.05	3.39	6.56	0.90
Swin^［30］	73.66	81.87	63.27	77.51	34.23
DNANet^［10］	78.87	88.05	71.24	83.21	57.68
ACM^［29］	79.16	87.06	70.83	82.92	58.51
UCFNet^［11］	83.66	87.65	75.29	86.55	25.28
IR-TransDet^［12］	82.09	87.13	73.21	84.53	41.02
基于交叉注意力	83.94	88.70	75.83	86.26	41.26
基于孪生网络	85.12	84.21	73.41	84.66	34.66
基于扩展语义图	84.91	87.27	75.56	86.08	42.54
基于自学习双通道	84.24	89.68	76.80	86.88	35.81

Table 2

Fig.20

Table 3

Results of environmental adaptability experimental metrics

算法名称	$P r e c i s i o n$ /%	$R e c a l l$ /%	$I o U$ /%	$F 1$ /%	$F P S$
Swin^［30］	0.42	0.05	0.05	0.09	35.41
DNANet^［10］	20.10	5.63	4.60	8.80	77.19
ACM^［29］	17.45	5.37	4.28	8.21	85.59
UCFNet^［11］	30.75	45.02	22.36	36.55	28.96
基于交叉注意力	29.85	60.14	24.92	39.90	49.68
基于孪生网络	36.64	44.80	25.24	40.31	44.00
基于扩展语义图	31.35	55.77	25.11	61.49	50.27
基于自学习双通道	28.71	61.87	24.39	39.22	47.43

Table 3

Fig.21

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图像序列	场景	目标大小	平均信杂比	图像数量
data2	天空	6×6、9×9	1.74	599
data4	天空	6×6、9×9	1.61	399
data5	大地	3×3	2.40	3 000
data6	大地	3×3	2.07	399
data8	大地	3×3	2.50	399
data10	天空、大地	3×3	0.77	502
data11	大地	3×3	1.93	745
data12	大地	3×3	2.54	1 500
data13	大地	3×3	1.36	763
data14	大地	3×3	0.65	1 426
data15	大地	3×3	2.23	751
data17	大地	3×3	0.99	500
data19	大地	3×3	1.41	1 374
data20	天空、大地	3×3	1.23	400
data21	大地	3×3	0.98	500
data22	大地	3×3	1.76	500

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Scene abstract semantic synthesis model and its application in infrared dim and small target detection

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