红外技术2024，Vol.46Issue(5)：556-564,9.
一种多分辨率特征提取红外图像语义分割算法
Multi-resolution Feature Extraction Algorithm for Semantic Segmentation of Infrared Images
摘要
Abstract
A multi-resolution feature extraction convolution neural network is proposed for the problem of inaccurate edge segmentation when existing image semantic segmentation algorithms process low-resolution infrared images.DeepLabv3+is used as the baseline network and adds a multi-resolution block,which contains both high and low resolution branches,to further aggregate the features in infrared images.In the low-resolution branch,a GPU friendly attention module is used to capture high-level global context information,and a multi-axis-gated multilayer perceptron module is added in this branch to extract the local and global information of infrared images in parallel.In the high resolution branch,the cross-attention module is used to propagate the global features learned on the low resolution branch to the high resolution branch,hence the high resolution branch can obtain stronger semantic information.The experimental results indicate that the segmentation accuracy of the algorithm on the dataset DNDS is better than that of the existing semantic segmentation algorithm,demonstrating the superiority of the proposed method.关键词
对偶分辨率模块/语义分割/DeepLabv3+/红外图像/注意力模块Key words
multi resolution block/semantic segmentation/deepLabv3+/infrared image/attention module分类
信息技术与安全科学引用本文复制引用
徐慧琳,赵鑫,于波,韦小牙,胡鹏..一种多分辨率特征提取红外图像语义分割算法[J].红外技术,2024,46(5):556-564,9.基金项目
安徽省教育厅重点项目(KJ2020A0289) （KJ2020A0289）
淮南市科技计划项目(2020186) （2020186）
安徽省教育厅重点项目(2022AH050801) （2022AH050801）
安徽理工大学青年教师科学研究基金(13200390). （13200390）
