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摘要:
面向小样本条件下的遥感图像目标检测任务，提出一种基于元学习的小样本遥感图像目标检测算法。针对遥感图像中目标尺度变化大、小样本条件下目标与背景易混淆的问题，在特征提取部分将单尺度重加权拓展为多尺度重加权模块，充分引入支持样本的先验知识以适应不同目标的尺度变化；为解决遥感图像目标类间相似性和类内差异性的问题，利用目标对于场景的依赖性设计了场景修正模块，对检出目标类别进行修正，并引入边际损失对特征空间内不同目标的特征分布进行约束。实验结果表明：所提算法在10样本任务设定上获得了较高的检测性能，在NWPU VHR-10和DIOR数据集新类别上的平均精度（mAP）分别达到了64.18%和37.27%。
Abstract:This study introduces meta-learning technology to propose a meta-learning-based few-shot object detection algorithm for the few-shot item detection job in remote sensing photos. The object and background are easily confused under the condition of large-scale changes and small samples in remote sensing images. To solve this issue, we expand the single-scale re-weighting into a multi-scale re-weighting module in the feature extraction part, where the prior knowledge of supporting samples can be adapted to different objects. In order to solve the problem of large inter-class similarities and intra-class differences among remote sensing objects, a scene correction module is designed by leveraging the dependence relationship between the object and scene to correct the detected object’s category. In order to restrict the feature distributions of various objects, we additionally incorporate the marginal loss to the feature space. Experimental results show that the proposed algorithm achieves high detection performance on the 10-shot task setting, achieving mean average precision (mAP) of 64.18% and 37.27% on the new category of NWPU VHR-10 and DIOR datasets, respectively.
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图 11 由于类内多样性和类间相似性造成的误检现象
Figure 11. False detections due to inter-class similarities and intra-class differences
表 1 重加权向量生成网络结构
Table 1. Structure of reweighted vector generation network
网络层 输出维度 是否重加权 标志 Input 4×512×512 否 Conv1 + Maxpooling 32×256×256 否 Conv2 + Maxpooling 64×128×128 否 Conv3 + Maxpooling 128×64×64 否 Conv4 + Maxpooling 256×32×32 否 Conv5 + CAM 256×1×1 是 通道 Route from Conv4 256×32×32 否 Conv6 + SAM 256×64×64 是 空间 Route with Conv4 256×32×32 否 Conv7 + Maxpooling 512×16×16 否 Conv8 + CAM 512×1×1 是 通道 Route from Conv7 512×16×16 否 Conv9 + SAM 512×32×32 是 空间 Route with Conv7 512×16×16 否 Conv10 + Maxpooling 1024 ×8×8否 Conv11 + CAM 1024 ×1×1是 通道 Route from Conv10 1024 ×8×8否 Conv9 + SAM 1024 ×16×16是 空间 
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表 2 多尺度重加权的有效性验证
Table 2. Effectiveness of the multiscale reweighting
样本/个 单尺度通道
mAP/%多尺度通道
mAP/%多尺度空间+通道
mAP/%3 13.17 26.36 29.52 5 26.22 49.03 51.44 10 37.10 61.74 62.85
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表 3 边际损失和场景修正的有效性验证
Table 3. Effectiveness of marginal loss and scene correction
样本/个 无约束
mAP/%边际损失
mAP/%场景修正+
边际损失mAP/%1 10.17 9.87 10.32 3 29.52 30.37 33.42 5 51.44 52.03 54.58 10 62.85 62.98 64.18
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表 4 本文算法与其他算法的性能对比
Table 4. Comparison results with other algorithms
% 数据集 类别 YOLOv4[14]算法 mAP Meta-YOLO[9]算法 mAP 本文算法mAP 5样本 10样本 3样本 5样本 10样本 3样本 5样本 10样本 NWPU VHR-10[24] airplane 14.22 13.27 20.17 20.52 17.87 44.53 51.11 baseball-diamond 26.05 14.73 43.64 74.38 55.69 83.45 90.71 tennis-court 4.45 11.52 14.85 16.42 26.71 35.76 50.73 mean 14.91 13.17 26.22 37.10 33.42 54.58 64.18 DIOR[25] airplane 2.55 7.56 9.04 15.04 11.93 15.93 19.16 baseballfield 32.25 27.35 33.37 45.63 30.57 39.06 51.29 Tennis-court 29.61 40.48 47.23 54.44 57.54 63.41 65.13 trainstation 1.79 8.62 9.27 7.98 11.35 13.52 19.25 windmill 4.84 9.05 13.35 18.21 20.83 27.56 31.53 mean 14.21 18.61 22.45 28.26 26.64 32.09 37.27
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