电子学报 ›› 2021, Vol. 49 ›› Issue (9): 1665-1674.DOI: 10.12263/DZXB.20200486
• 学术论文 • 下一篇
赵琰, 赵凌君, 匡纲要
收稿日期:
2020-05-21
修回日期:
2020-11-30
出版日期:
2021-09-25
发布日期:
2021-09-25
作者简介:
基金资助:
Yan ZHAO, Ling-jun ZHAO, Gang-yao KUANG
Received:
2020-05-21
Revised:
2020-11-30
Online:
2021-09-25
Published:
2021-09-25
摘要:
针对合成孔径雷达(Synthetic Aperture Radar,SAR)图像中飞机目标散射点离散化程度高,周围背景干扰复杂,现有算法对飞机浅层语义特征表征能力弱等问题,本文提出了基于注意力特征融合网络(Attention Feature Fusion Network,AFFN)的SAR 图像飞机目标检测算法.通过引入瓶颈注意力模块(Bottleneck Attention Module,BAM),本文在AFFN中构建了包含注意力双向特征融合模块(Attention Bidirectional Feature Fusion Module,ABFFM)与注意力传输连接模块(Attention Transfer Connection Block,ATCB)的注意力特征融合策略并合理优化了网络结构,提升了算法对飞机离散化散射点浅层语义特征的提取与判别.基于自建的Gaofen-3 与TerraSAR-X 卫星图像混合飞机目标实测数据集,实验对AFFN与基于深度学习的通用目标检测以及SAR图像特定目标检测算法进行了比较,其结果验证了AFFN对SAR图像飞机目标检测的准确性与高效性.
中图分类号:
赵琰, 赵凌君, 匡纲要. 基于注意力机制特征融合网络的SAR图像飞机目标快速检测[J]. 电子学报, 2021, 49(9): 1665-1674.
Yan ZHAO, Ling-jun ZHAO, Gang-yao KUANG. Attention Feature Fusion Network for Rapid Aircraft Detection in SAR Images[J]. Acta Electronica Sinica, 2021, 49(9): 1665-1674.
网络 | 锚点框尺寸 | 32,64,128 |
---|---|---|
锚点框比例 | 1,1.414,0.707 | |
r (BAM) | 16 | |
训练 | 迭代总次数(Epochs) | 200 |
小批次(Minibatch) | 12 | |
初始学习率 | 5e-4 | |
优化器 | Adam | |
权重衰减值 | 1e-4 |
表1 网络超参数设置
网络 | 锚点框尺寸 | 32,64,128 |
---|---|---|
锚点框比例 | 1,1.414,0.707 | |
r (BAM) | 16 | |
训练 | 迭代总次数(Epochs) | 200 |
小批次(Minibatch) | 12 | |
初始学习率 | 5e-4 | |
优化器 | Adam | |
权重衰减值 | 1e-4 |
算法类别 | 网络名称 | P | R | F1 | AP | Params(M) | FLOPs (G) | FPS |
---|---|---|---|---|---|---|---|---|
通用目标 两阶段算法 | Faster-RCNN[ | 0.736 | 0.790 | 0.762 | 0.749 | 136.689 | 149.228 | 14 |
Cascade-RCNN[ | 0.707 | 0.733 | 0.720 | 0.697 | 304.076 | 179.934 | 8 | |
FPN[ | 0.778 | 0.810 | 0.794 | 0.814 | 120.690 | 134.150 | 16 | |
通用目标 单阶段算法 | SSD[ | 0.798 | 0.778 | 0.788 | 0.820 | 23.801 | 30.570 | 250 |
RFB[ | 0.854 | 0.764 | 0.806 | 0.838 | 31.471 | 34.422 | 100 | |
LRF[ | 0.899 | 0.740 | 0.812 | 0.809 | 65.774 | 48.114 | 91 | |
YOLOv3[ | 0.502 | 0.791 | 0.614 | 0.630 | 61.529 | 32.687 | 15 | |
RefineDet[ | 0.856 | 0.806 | 0.830 | 0.843 | 33.915 | 37.426 | 90 | |
RetinaNet[ | 0.837 | 0.764 | 0.799 | 0.789 | 135.366 | 111.994 | 28 | |
SAR图像 特定目标 检测算法 | PADN[ | 0.820 | 0.777 | 0.798 | 0.806 | 102.163 | 123.562 | 33 |
DAPN[ | 0.789 | 0.830 | 0.809 | 0.831 | 128.230 | 167.733 | 12 | |
文献[ | 0.520 | 0.315 | 0.395 | 0.189 | 4.314 | 0.328 | 0.5 | |
文献[ | 0.544 | 0.238 | 0.331 | 0.132 | 6.158 | 11.661 | 0.5 | |
文献[ | 0.706 | 0.235 | 0.353 | 0.200 | 134.309 | 37.279 | 0.4 | |
文献[ | 0.644 | 0.807 | 0.716 | 0.741 | 102.357 | 97.549 | 2 | |
本文算法 | AFFN | 0.856 | 0.873 | 0.864 | 0.871 | 32.190 | 39.215 | 91 |
表2 不同算法对SAR图像飞机目标测试结果
算法类别 | 网络名称 | P | R | F1 | AP | Params(M) | FLOPs (G) | FPS |
---|---|---|---|---|---|---|---|---|
通用目标 两阶段算法 | Faster-RCNN[ | 0.736 | 0.790 | 0.762 | 0.749 | 136.689 | 149.228 | 14 |
Cascade-RCNN[ | 0.707 | 0.733 | 0.720 | 0.697 | 304.076 | 179.934 | 8 | |
FPN[ | 0.778 | 0.810 | 0.794 | 0.814 | 120.690 | 134.150 | 16 | |
通用目标 单阶段算法 | SSD[ | 0.798 | 0.778 | 0.788 | 0.820 | 23.801 | 30.570 | 250 |
RFB[ | 0.854 | 0.764 | 0.806 | 0.838 | 31.471 | 34.422 | 100 | |
LRF[ | 0.899 | 0.740 | 0.812 | 0.809 | 65.774 | 48.114 | 91 | |
YOLOv3[ | 0.502 | 0.791 | 0.614 | 0.630 | 61.529 | 32.687 | 15 | |
RefineDet[ | 0.856 | 0.806 | 0.830 | 0.843 | 33.915 | 37.426 | 90 | |
RetinaNet[ | 0.837 | 0.764 | 0.799 | 0.789 | 135.366 | 111.994 | 28 | |
SAR图像 特定目标 检测算法 | PADN[ | 0.820 | 0.777 | 0.798 | 0.806 | 102.163 | 123.562 | 33 |
DAPN[ | 0.789 | 0.830 | 0.809 | 0.831 | 128.230 | 167.733 | 12 | |
文献[ | 0.520 | 0.315 | 0.395 | 0.189 | 4.314 | 0.328 | 0.5 | |
文献[ | 0.544 | 0.238 | 0.331 | 0.132 | 6.158 | 11.661 | 0.5 | |
文献[ | 0.706 | 0.235 | 0.353 | 0.200 | 134.309 | 37.279 | 0.4 | |
文献[ | 0.644 | 0.807 | 0.716 | 0.741 | 102.357 | 97.549 | 2 | |
本文算法 | AFFN | 0.856 | 0.873 | 0.864 | 0.871 | 32.190 | 39.215 | 91 |
网络名称 | P | R | F1 | AP | Params(M) | FLOPs(G) | FPS |
---|---|---|---|---|---|---|---|
AFFN | 0.856 | 0.873 | 0.864 | 0.871 | 32.190 | 39.215 | 91 |
AFFN-ATCB | 0.850 | 0.869 | 0.859 | 0.870 | 32.172 | 39.201 | 95 |
AFFN-ABFFM | 0.842 | 0.886 | 0.863 | 0.871 | 30.402 | 38.504 | 95 |
AFFN(TCB) | 0.840 | 0.857 | 0.848 | 0.865 | 30.385 | 38.490 | 100 |
RefineDet | 0.856 | 0.806 | 0.830 | 0.843 | 33.915 | 37.426 | 90 |
表3 注意力特征融合网络对算法性能的影响
网络名称 | P | R | F1 | AP | Params(M) | FLOPs(G) | FPS |
---|---|---|---|---|---|---|---|
AFFN | 0.856 | 0.873 | 0.864 | 0.871 | 32.190 | 39.215 | 91 |
AFFN-ATCB | 0.850 | 0.869 | 0.859 | 0.870 | 32.172 | 39.201 | 95 |
AFFN-ABFFM | 0.842 | 0.886 | 0.863 | 0.871 | 30.402 | 38.504 | 95 |
AFFN(TCB) | 0.840 | 0.857 | 0.848 | 0.865 | 30.385 | 38.490 | 100 |
RefineDet | 0.856 | 0.806 | 0.830 | 0.843 | 33.915 | 37.426 | 90 |
网络名称 | P | R | F1 | AP | Params(M) | FLOPs(G) | FPS |
---|---|---|---|---|---|---|---|
AFFN(BAM) | 0.856 | 0.873 | 0.864 | 0.871 | 32.190 | 39.215 | 91 |
AFFN(SE) | 0.835 | 0.865 | 0.850 | 0.868 | 32.189 | 39.197 | 98 |
AFFN(CBAM) | 0.846 | 0.872 | 0.859 | 0.867 | 32.172 | 39.198 | 85 |
AFFN(Basic) | 0.856 | 0.853 | 0.854 | 0.865 | 32.155 | 39.197 | 100 |
表4 不同注意力机制对算法性能的影响
网络名称 | P | R | F1 | AP | Params(M) | FLOPs(G) | FPS |
---|---|---|---|---|---|---|---|
AFFN(BAM) | 0.856 | 0.873 | 0.864 | 0.871 | 32.190 | 39.215 | 91 |
AFFN(SE) | 0.835 | 0.865 | 0.850 | 0.868 | 32.189 | 39.197 | 98 |
AFFN(CBAM) | 0.846 | 0.872 | 0.859 | 0.867 | 32.172 | 39.198 | 85 |
AFFN(Basic) | 0.856 | 0.853 | 0.854 | 0.865 | 32.155 | 39.197 | 100 |
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