南京航空航天大学学报2024，Vol.56Issue(2)：291-299,9.DOI:10.16356/j.1005-2615.2024.02.011
基于深度学习域适应的飞机结冰图像气泡提取方法
Bubble Extraction Method from Aircraft Icing Images Based on Deep Learning Domain Adaptation
摘要
Abstract
The extraction of bubbles from ice micrographs using deep learning methods requires a significant amount of annotated data.However,the manual annotation of bubbles presents a significant challenge in this regard.A domain-adaptive extraction method is proposed,which utilizes the CycleGAN style transfer network and the Attention U-Net image segmentation network.In this method,the image generated by simulating the shape of the bubble is used as the source domain,and the icing microscopic image is used as the target domain.The source domain image is converted into the target domain style through CycleGAN,and the Attention U-Net network is trained using the style-converted source domain dataset.The two cases of unlabeled icing images and a few labeled images are verified by comparative experiments.Experimental results show that the unsupervised extraction of air bubbles from icing microscopic images can be achieved without annotated images,and the method can achieve more accurate air bubble extraction with only a few annotated images.关键词
动态结冰/气泡提取/图像分割/域适应/Attention U-NetKey words
dynamic icing/bubble extraction/image segmentation/domain adaptation/Attention U-Net分类
信息技术与安全科学引用本文复制引用
赵红梅,彭博,周志宏,易贤..基于深度学习域适应的飞机结冰图像气泡提取方法[J].南京航空航天大学学报,2024,56(2):291-299,9.基金项目
国家自然科学基金重点基金(12132019) （12132019）
国家重大科技专项(J2019-III-0010-0054) （J2019-III-0010-0054）
国家自然科学基金面上基金(12172372). （12172372）
