计算机工程与应用2025，Vol.61Issue(4)：222-229,8.DOI:10.3778/j.issn.1002-8331.2310-0064
结合CNN-Transformer的跨模态透明物体分割
Cross-Modal Transparent Object Segmentation Combining CNN-Transformer
摘要
Abstract
Transparent objects have visual characteristics such as high transparency,glossiness and special texture,which make the boundary between the object and the background often blurred,making it difficult for traditional image segmen-tation algorithms to accurately recognize and segment them,so this paper proposes a cross-modal semantic segmentation algorithm for transparent objects,CTNet,which combines CNN-Transformer.The algorithm adopts the encoding-decoding structure of CNN and Transformer hybrid network to predict the category and location of transparent objects across modal-ities,CNN is used to extract image features,and Transformer is used for multimodal fusion transformer(MFT).The enhanced boundary attention module(EBAM)is designed to improve the image edge segmentation ability.A multi-scale fusion decoding structure is proposed to reduce the blurred features.The mean absolute error(MAE)of CTNet is 3.3%in the RGB-T-Glass dataset,and the intersection over union(IOU)is 90.18%and 95.00%in the test sets with transparent objects and without transparent objects,respectively.On the GDD dataset,the MAE is 6.9%and the IOU is 87.6%.The results show that CTNet successfully realizes accurate segmentation of transparent objects using visible and thermal infrared images,and meets the requirements of accuracy and robustness when segmenting transparent objects in the target task.关键词
CNN-Transformer/多模态/透明物体/语义分割/特征融合Key words
CNN-Transformer/multimodal/transparent objects/semantic segmentation/feature fusion分类
信息技术与安全科学引用本文复制引用
潘惟兰,张荣芬,刘宇红,张吉友,孙龙..结合CNN-Transformer的跨模态透明物体分割[J].计算机工程与应用,2025,61(4):222-229,8.基金项目
贵州省基础研究(自然科学)项目(黔科合基础-ZK[2021]重点001). （自然科学）
