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SAR Ship Detection in Complex Scenes Based on Adaptive Anchor Assignment and IOU Supervise
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摘要: 针对复杂场景舰船检测中正负样本分配不合理以及定位质量较差的问题，该文提出了一种基于自适应锚框分配与交并比(IOU)监督的复杂场景合成孔径雷达(SAR)舰船检测方法(A3-IOUS-Net)。首先，A3-IOUS-Net提出了自适应锚框分配，建立概率分布模型来自适应地将锚框分配为正负样本，增强了复杂场景下的舰船样本学习能力。然后，A3-IOUS-Net提出了IOU监督，在预测头部增加IOU预测分支来监督检测框定位质量，使得网络能够精确定位复杂场景下的舰船目标。此外，在该IOU预测分支中引入了坐标注意力模块，抑制了背景杂波干扰，进一步提高了检测精度。在公开的SAR舰船检测数据集(SSDD)的实验结果表明，A3-IOUS-Net在复杂场景中的平均精度(AP)值为82.04%，优于其他15种对比模型。Abstract: This study aims to address the unreasonable assignment of positive and negative samples and poor localization quality in ship detection in complex scenes. Therefore, in this study, a Synthetic Aperture Radar (SAR) ship detection network (A3-IOUS-Net) based on adaptive anchor assignment and Intersection over Union (IOU) supervise in complex scenes is proposed. First, an adaptive anchor assignment mechanism is proposed, where a probability distribution model is established to adaptively assign anchors as positive and negative samples to enhance the ship samples’ learning ability in complex scenes. Then, an IOU supervise mechanism is proposed, which adds an IOU prediction branch in the prediction head to supervise the localization quality of detection boxes, allowing the network to accurately locate the SAR ship targets in complex scenes. Furthermore, a coordinate attention module is introduced into the prediction branch to suppress the background clutter interference and improve the SAR ship detection accuracy. The experimental results on the open SAR Ship Detection Dataset (SSDD) show that the Average Precision (AP) of A3-IOUS-Net in complex scenes is 82.04%, superior to the other 15 comparison models.
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图 2 预测框和真实边框之间的IOU示意图
Figure 2. Schematic diagram of IOU between the predicted box and the ground truth box
图 3 经典锚框分配准则下SAR舰船复杂场景的锚框分布
Figure 3. The detection result of complex scenes under the classical anchor box allocation criteria
图 7 预测框和真实边框之间的GIOU示意图
Figure 7. Schematic diagram of GIOU between the predicted box and the ground truth box
图 8 不同方法在不同场景上的精度-召回率曲线
Figure 8. Precision-Recall curves of different methods in different scenes
图 9 A3-IOUS-Net和次优模型Libra R-CNN的复杂场景检测结果对比
Figure 9. Detection performance comparison of A3-IOUS-Net and second-best model Libra R-CNN under complex scenes
1 自适应锚框分配基本流程
1. Basic process of adaptive anchor assignment
输入：一组真实边框$\mathcal{G}$，一组锚框$\mathcal{A}$，一组来自第$ { i } $金字塔层级的
锚框${\mathcal{A} }_{i}$，金字塔层级$ \mathcal{L} $，每个金字塔层级的候选锚框$\mathcal{K}$过程： 1：do 自适应锚框分配 2： $\mathcal{P} \leftarrow \varnothing, \;\mathcal{N} \leftarrow \varnothing$ 3： for $g \in \mathcal{G}$ do 4： $ \mathcal{A}_{g} \leftarrow $锚框获取$(\mathcal{A}, g, \mathcal{G})$ 5： $ \mathcal{C}_{g} \leftarrow \varnothing $ 6： for $ i=1 $ to $ \mathcal{L} $ do 7： $\mathcal{A}_{i}^g \leftarrow \mathcal{A}_{i} \cap \mathcal{A}_{g}$ 8： $\mathcal{S}_{i} \leftarrow$计算锚框分数$\left(\mathcal{A}_{i}^{g}, g\right)$ 9： $t_{i} \leftarrow$收集分数前$\mathcal{Q}$锚框$ \left(s_{i}, \mathcal{K}\right) $ 10： $\mathcal{C}_{g}^{i} \leftarrow\left\{a_{j} \in \mathcal{A}_{i}^{g} \mid t_{i} \le s_{j} \in \mathcal{S}_{i}\right\}$ 11： $C_{g} \leftarrow \mathcal{C}_{g} \cup C_{g}^{i}$ 12： end 13： $ \mathcal{B}, \mathcal{F} \leftarrow $高斯混合分布建模$\left(\mathcal{C}_{g}, 2\right)$ 14： $\mathcal{N}_{g,} \mathcal{P}_{g} \leftarrow$锚框分配$\left(\mathcal{C}_{g}, \mathcal{B}, \mathcal{F}\right)$ 15： $\mathcal{P} \leftarrow \mathcal{P} \cup \mathcal{P}_{g}, \;\mathcal{N} \leftarrow \mathcal{N} \cup \mathcal{N}_{g}, \;\mathcal{I} \leftarrow \mathcal{I}\cup\left(\mathcal{C}_{g} - \mathcal{P}_{g} - \mathcal{N}_{g}\right)$ 16：end 17：$\mathcal{N} \leftarrow \mathcal{N}\cup(\mathcal{A}-P-\mathcal{N}-\mathcal{I})$ 18：end 输出：一组正样本$ \mathcal{P} $，一组负样本$ \mathcal{N} $，一组忽略样本$\mathcal{ I} $ 
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2 结合IOU监督的NMS后处理基本流程
2. Basic process of NMS combined with IOU supervision
输入：初始检测框集合$ \mathcal{B}=\left\{b_{1}, b_{2}, \cdots, b_{N}\right\} $，初始检测框分类分
数集合${\mathcal{S}}_{\mathrm{cls} }=\left\{\mathrm{cls}_{1,}, \mathrm{cls}_{2,} \cdots, \mathrm{cls}_{N}\right\}$，初始检测框IOU分数
集合${\mathcal{S} }_{\mathrm{IOU} }=\left\{\operatorname{IOU}_{1}, \operatorname{IOU}_{2,} \cdots, \operatorname{IOU}_{N}\right\}$，IOU阈值$N_{{\rm{t}}}$。过程： 1：do 联合分类得分和IOU得分 2： $\mathcal{S} \leftarrow\{\;\}$ 3： ${\mathcal{S} }={\mathcal{S} }_{ {\rm{cls} } }^{1/2}\cdot {\mathcal{S} }_{ {\rm{IOU} } }^{1 / 2}$ IOU监督 4：end 5：do NMS后处理 6： $\mathcal{D} \leftarrow\{\;\}$ 7： while $ \mathcal{B} \neq \varnothing $ do 8： $m \leftarrow \text {arg\,max} \;{\mathcal{S} }$ 9： $\mathcal{M} \leftarrow b_{m}$ 10： $ \mathcal{D} \leftarrow \mathcal{D} \cup \mathcal{M} ; \mathcal{B} \leftarrow \mathcal{B}-\mathcal{M} $ 11： for $b_{i} \;\text { in }\; \mathcal{B}$ do 12： if ${ {\rm{IOU} } }\left(\mathcal{M}, b_{i}\right) \ge N_{{\rm{t}}}$ then 13： $\mathcal{B} \leftarrow \mathcal{B}-b_{i} ; \;\mathcal{S} \leftarrow \mathcal{S}-s_{i}$ 14： end 15： end 16： end 17：end 输出：NMS处理后的检测框集合$\mathcal{ D}$，NMS处理后的检测分数集
合$\mathcal{S}$
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表 1 SSDD数据集信息概览
Table 1. Information of SSDD
参数 指标 SAR卫星 RadarSat-2, TerraSAR-X, Sentinel-1 SAR图像数量 1160 图像平均尺寸 500像素×500像素 训练集测试集比例 8∶2 极化模式 HH, VV, VH, HV 分辨率 1～15 m 地点 中国烟台、印度维萨卡帕特南 海况 良好、较差 场景 复杂靠岸、简单离岸 舰船数量 2587 最小尺寸舰船 66像素 最大尺寸舰船 78597像素
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表 2 A3-IOUS-Net和其他方法性能对比
Table 2. Comparison of performance of A3-IOUS-Net and other methods
方法 全部场景(%) 复杂靠岸场景(%) 简单离岸场景(%) Params (M) R P AP R P AP R P AP RetinaNet[17] 83.15 89.37 82.23 58.14 76.92 56.15 94.65 93.65 94.01 30.94 ATSS[28] 86.81 87.13 85.78 62.79 76.06 59.07 97.86 91.04 97.50 30.86 DCN[29] 89.38 72.62 86.67 72.67 55.80 63.09 97.06 81.03 95.91 41.49 PANET[30] 92.49 79.65 91.06 77.91 61.19 71.95 99.20 89.40 98.76 44.16 Faster R-CNN[31] 86.81 81.87 85.15 71.51 63.73 65.29 93.85 90.93 93.16 40.61 Cascade R-CNN[32] 87.18 89.14 86.40 69.19 73.01 65.36 95.45 96.23 95.29 68.42 Dynamic R-CNN[33] 89.19 85.59 88.18 69.77 69.77 65.42 98.13 92.44 97.83 40.61 Double-Head R-CNN[34] 92.31 84.28 91.22 78.49 68.53 73.72 98.66 92.02 98.29 46.20 Swim Transformer[35] 89.01 89.17 88.42 75.00 73.71 71.95 95.45 96.49 95.32 72.55 Libra R-CNN[36] 93.22 83.99 91.59 80.81 65.26 73.66 98.93 94.15 98.43 40.88 ARPN[37] 89.01 88.04 88.06 72.67 73.96 68.49 96.52 94.26 96.28 41.17 Quad-FPN[38] 92.12 79.46 90.91 77.33 59.64 70.79 98.93 90.24 98.73 46.44 HR-SDNet[39] 91.76 87.89 90.88 76.16 70.43 71.83 98.93 96.35 98.79 90.92 GWFFE-Net[15] 92.86 71.81 91.34 81.98 53.61 75.71 97.86 82.62 97.45 61.48 SER Faster R-CNN[40] 93.04 77.79 91.52 81.40 59.32 74.88 98.40 88.25 97.96 41.74 A3-IOUS-Net 95.05 89.18 94.05 86.05 79.57 82.04 99.20 93.69 98.83 31.08
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表 3 A3-IOUS-Net是否使用自适应锚框分配机制对精度的影响(%)
Table 3. Effect of whether A3-IOUS-Net using adaptive anchor assignment mechanism (%)
自适应锚框分配 全部场景 复杂靠岸场景 简单离岸场景 R P AP R P AP R P AP × 91.76 89.46 90.72 76.16 75.72 70.87 98.93 95.61 98.76 √ 95.05 89.18 94.05 86.05 79.57 82.04 99.20 93.69 98.83
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表 4 自适应锚框分配机制使用不同概率分布模型对精度的影响(%)
Table 4. Effect of adaptive anchor assignment mechanism using different probability distribution models (%)
概率分布模型类型 全部场景 复杂靠岸场景 简单离岸场景 R P AP R P AP R P AP 狄利克雷混合分布 87.36 88.17 86.50 64.53 77.62 62.39 97.86 91.96 97.28 T混合分布 89.01 92.57 88.17 69.77 83.33 66.82 97.86 96.06 97.46 β混合分布 91.76 85.35 91.01 76.74 70.21 73.29 98.66 92.48 98.40 高斯混合分布 95.05 89.18 94.05 86.05 79.57 82.04 99.20 93.69 98.83
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表 5 A3-IOUS-Net是否使用IOU监督机制对精度的影响(%)
Table 5. Effect of whether A3-IOUS-Net using IOU supervise mechanism (%)
IOU监督 全部场景 复杂靠岸场景 简单离岸场景 R P AP R P AP R P AP × 89.38 90.37 87.19 70.93 78.21 64.66 97.86 95.31 96.72 √ 95.05 89.18 94.05 86.05 79.57 82.04 99.20 93.69 98.83
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表 6 IOU监督机制使用不同IOU预测损失函数对精度的影响(%)
Table 6. Effect of IOU supervise mechanism using different IOU prediction loss functions (%)
损失函数类型 全部场景 复杂靠岸场景 简单离岸场景 R P AP R P AP R P AP IOU Loss 92.67 89.08 91.68 78.49 79.41 74.50 99.20 93.22 98.80 GIOU Loss 95.05 89.18 94.05 86.05 79.57 82.04 99.20 93.69 98.83
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表 7 IOU监督机制是否使用坐标注意力模块对精度的影响(%)
Table 7. Effect of whether IOU supervise mechanism using coordinate attention module (%)
坐标注意力 全部场景 复杂靠岸场景 简单离岸场景 R P AP R P AP R P AP × 93.59 86.32 92.74 80.81 70.20 76.64 99.47 94.42 99.24 √ 95.05 89.18 94.05 86.05 79.57 82.04 99.20 93.69 98.83
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