Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (20): 630702.doi: 10.7527/S1000-6893.2024.30702
• Aeronautics Computing and Simulation Technique • Previous Articles
Shaoyi LI1, Yaqi ZHANG1, Yue CHENG2, Xi YANG1(), Liang ZHANG3, Jian LIN1, Zhongjie MENG1
Received:
2024-05-20
Revised:
2024-06-11
Accepted:
2024-06-28
Online:
2024-07-11
Published:
2024-07-01
Contact:
Xi YANG
E-mail:nwpuyx@163.com
Supported by:
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.
Table 1
Image sequence information
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 |
Table 2
Quantitative experimental results of infrared dim and small target detection
算法名称 | |||||
---|---|---|---|---|---|
MAXMEAN[ | 0.014 | 10.18 | 0.014 | 0.029 | 2.64 |
AAGD[ | 11.87 | 4.02 | 3.09 | 6.00 | 2.53 |
ADMD[ | 12.40 | 6.77 | 4.58 | 8.76 | 2.49 |
ILCM[ | 0.14 | 93.25 | 0.14 | 0.29 | 2.71 |
MPCM[ | 0.009 7 | 9.19 | 0.009 7 | 0.019 | 2.29 |
RLCM[ | 7.71 | 54.76 | 7.25 | 13.51 | 0.68 |
TLLCM[ | 17.36 | 8.09 | 5.84 | 11.04 | 1.49 |
HB-MLCM[ | 16.22 | 8.74 | 6.02 | 11.36 | 2.49 |
MLCM-LEF[ | 24.04 | 8.82 | 6.09 | 12.90 | 0.82 |
WSLCM[ | 27.63 | 10.22 | 8.06 | 14.92 | 0.56 |
Top-Hat[ | 1.85 | 11.06 | 1.61 | 3.17 | 2.78 |
LIG[ | 6.13 | 7.05 | 3.39 | 6.56 | 0.90 |
Swin[ | 73.66 | 81.87 | 63.27 | 77.51 | 34.23 |
DNANet[ | 78.87 | 88.05 | 71.24 | 83.21 | 57.68 |
ACM[ | 79.16 | 87.06 | 70.83 | 82.92 | 58.51 |
UCFNet[ | 83.66 | 87.65 | 75.29 | 86.55 | 25.28 |
IR-TransDet[ | 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 3
Results of environmental adaptability experimental metrics
算法名称 | |||||
---|---|---|---|---|---|
Swin[ | 0.42 | 0.05 | 0.05 | 0.09 | 35.41 |
DNANet[ | 20.10 | 5.63 | 4.60 | 8.80 | 77.19 |
ACM[ | 17.45 | 5.37 | 4.28 | 8.21 | 85.59 |
UCFNet[ | 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 |
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