红外技术2026，Vol.48Issue(5)：535-543,9.
跨模态注意力引导的RGB-T融合分割网络
Cross-modal Attention Guided RGB-T Fusion Segmentation Network
摘要
Abstract
In recent years,significant advancements have been achieved in computer vision using deep learning.However,single-modal RGB images have limitations in urban scenes and are easily affected by lighting and adverse weather conditions,resulting in low robustness.Image semantic segmentation tasks require detailed and highly discriminative semantic information,but continuous downsampling during feature extraction can lead to the loss of detailed features.To address these issues,this study proposes a CMASeg cross-modal attention-guided RGB-T fusion segmentation network.This network uses ResNet-152 as the encoder and enhances the features using a channel-spatial attention module.It also employs a cross-modal feature refinement module to exploit the complementary information of RGB-T images,thereby achieving effective fusion and feature extraction of multimodal data.Experimental results show that CMASeg achieved a segmentation accuracy of 71.7%mean accuracy and 55.9%mean intersection over union on the publicly available MFNet dataset,outperforming existing algorithms.The proposed method performs well in urban scenes and provides a new solution for semantic image segmentation tasks.关键词
通道注意力/空间注意力/多模态数据融合/深度卷积神经网络/语义分割/红外图像Key words
channel attention/spatial attention/multimodal data fusion/deep convolutional neural network/semantic segmentation/infrared image分类
信息技术与安全科学引用本文复制引用
董莹新,王茂宁,钟羽中..跨模态注意力引导的RGB-T融合分割网络[J].红外技术,2026,48(5):535-543,9.基金项目
四川省科技计划项目(2022YFG0084). （2022YFG0084）
