山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (2): 99-106.doi: 10.6040/j.issn.1672-3961.0.2021.329
尹旭1,刘兆英1,张婷1*,李玉鑑1,2
YIN Xu1, LIU Zhaoying1, ZHANG Ting1*, LI Yujian1,2
摘要： 为降低获取像素级标签的成本,提出一种基于弱监督和半监督学习的红外舰船分割方法,在残差网络(residual network, ResNet)的基础上,设计一个自适应定位模块,并使用相似损失、前景损失和背景损失训练自适应定位模块,生成舰船定位图;利用少量像素级标签数据和大量定位图数据交替训练显著性网络生成显著图;用条件随机场优化显著图,并结合图像级标签生成伪标签图像,使用伪标签图像训练分割网络,得到红外舰船的分割结果。在红外舰船数据集上的平均交并比为71.18%,与当前其他先进方法进行对比,平均交并比提高了9.47%,试验结果表明自适应定位模块能够有效定位红外舰船,交替训练方法可以使红外舰船的边缘更准确。
中图分类号:
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