基于注意力和特征融合的遥感图像目标检测模型 
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Remote Sensing Image Target Detection Model Based on Attention and Feature Fusion
陕西师范大学计算机科学学院, 陕西 西安 710119
图 & 表
图 1. AFFSSD模型的结构
Fig. 1. Structure of the AFFSSD model
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图 2. 不同模型的飞机检测结果。(a) SSD模型;(b) AFFSSD模型
Fig. 2. Test results of different models of aircraft. (a) SSD model; (b) AFFSSD model
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图 3. 不同模型的车辆检测结果。(a) SSD模型;(b)AFFSSD模型
Fig. 3. Test results of different models of vehicle. (a) SSD model; (b) AFFSSD model
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图 4. 两种模型的部分检测结果。(a) SSD模型; (b) AFFSSD模型
Fig. 4. Partial test results of the two models. (a) SSD model; (b) AFFSSD model
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表 1AFFSSD模型的具体参数
Table1. Specific parameter of the AFFSSD model
| Branch | Layer name | Output size | Operation of convolution |
|---|
| Detection branch | conv4_3 | 64×64×512 | | | fc6 | 32×32×1024 | | | fc7 | 32×32×1024 | | | conv8_2 | 16×16×512 | | | conv9_2 | 8×8×256 | | | conv10_2 | 4×4×256 | | | conv11_2 | 2×2×256 | | | conv12_2 | 1×1×256 | -- | | Attention branch | att_conv4 | 8×8×256 | | | att_conv5 | 8×8×2 | -- |
|
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表 2UCAS-AOD数据集中的车辆尺寸
Table2. Vehicle dimension in the UCAS-AOD data set
| Scale /pixel | Scale1 (<100) | Scale2 (100--200) | Scale3 (200--300) | Scale4 (>300) | Total |
|---|
| Number(vehicle) | 3704 | 1986 | 967 | 457 | 7114 | | Number(aircraft) | 1028 | 1438 | 2593 | 2369 | 7482 |
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表 3不同方法在UCAS-AOD数据集中的检测结果
Table3. Detection results of different methods in UCAS-AOD data set
| Method | AP of plane /% | AP of small-vehicle /% | mAP /% | S /s |
|---|
| SSD | 88.13 | 85.09 | 86.61 | 0.36 | | Ref.[17] | 90.66 | 88.17 | 89.41 | 0.34 | | AFFSSD | 93.70 | 91.34 | 92.52 | 0.26 |
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表 4两种模型对不同尺度目标的检测结果
Table4. Detection results of the two models on different scale targets
| Method | SSD | AFFSSD |
|---|
| AP of scale1/% | 57.11 | 62.07 | | AP of scale2/% | 64.09 | 70.31 | | AP of scale3/% | 69.02 | 72.65 | | AP of scale4/% | 69.71 | 71.33 | | S /s | 38.90 | 38.10 |
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表 5不同模型在NWPU VHR-10数据集中的检测结果
Table5. Detection results of different models in the NWPU VHR-10 data set unit: %
| Model | RICNN[18] | SSD | DSSD[19] | Ref.[20] | Deformable R-FCN[21] | Faster R-CNN[22] | AFFSSD |
|---|
| Aircraft | 88.35 | 84.32 | 86.50 | 95.20 | 87.30 | 94.60 | 87.02 | | Ship | 77.34 | 62.90 | 65.40 | 79.70 | 81.40 | 82.30 | 83.50 | | Oil tank | 85.27 | 78.25 | 90.30 | 73.70 | 63.60 | 65.32 | 80.69 | | Baseball diamond | 88.12 | 89.33 | 89.60 | 96.40 | 90.40 | 95.50 | 96.02 | | Tennis court | 40.83 | 79.41 | 85.10 | 71.60 | 81.60 | 81.90 | 80.32 | | Basketball court | 58.45 | 87.69 | 80.40 | 72.10 | 74.10 | 89.70 | 90.10 | | Ground track field | 86.73 | 80.61 | 78.20 | 99.70 | 90.30 | 92.40 | 81.36 | | Harbor | 68.60 | 71.37 | 70.50 | 73.20 | 75.30 | 72.40 | 75.80 | | Bridge | 61.51 | 65.35 | 68.20 | 57.00 | 71.40 | 57.50 | 72.03 | | Vehicle | 71.10 | 62.30 | 74.20 | 72.00 | 75.50 | 77.80 | 78.01 | | mAP | 72.63 | 76.15 | 78.84 | 79.06 | 79.09 | 80.94 | 82.49 |
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汪亚妮, 汪西莉. 基于注意力和特征融合的遥感图像目标检测模型[J]. 激光与光电子学进展, 2021, 58(2): 0228003. Yani Wang, Xili Wang. Remote Sensing Image Target Detection Model Based on Attention and Feature Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0228003.
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