计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 209-213.doi: 10.11896/jsjkx.220500153
华杰, 刘学亮, 赵烨
HUA Jie, LIU Xueliang, ZHAO Ye
摘要: 小样本目标检测旨在通过少量的样本学习来训练目标检测模型,现有的小样本目标检测方法大多基于经典的目标检测算法。在二阶段的检测方法中,由于新类别样本数量少,产生了许多无关的边界框,导致候选区域的准确率较低。为了解决这个问题,提出了一种基于特征融合的小样本目标检测算法FF-FSOD。该方法采用特征融合的方法进行数据增强,对新类别样本进行补充,扩大样本的覆盖范围,同时引入FPN网络进行多尺度特征提取,再对RPN网络进行改进,引入支持集图像分支,计算支持集图像特征与查询集图像特征的深度互相关性,得到注意力特征图,进而获得更精确的候选框。所提模型的有效性在MS COCO和FSOD数据集上得到了验证,实验结果表明,该方法获得了更精准的候选框,进而提升了检测精度。
中图分类号:
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