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

针对现有图像语义分割算法在对低分辨率红外图像进行分割时存在准确率不高的问题,提出了一种多分辨率特征提取算法。该算法以DeepLabv3+为基准网络,添加了一组对偶分辨率模块,该模块包含低分辨率分支和高分辨率分支,以进一步聚合红外图像特征。低分辨率分支采用GPU友好的注意力模块捕获高层全局上下文信息,同时引入一个多轴门控感知机模块并行提取红外图像局部信息和全局信息;高分辨率分支采用跨分辨率注意力模块将低分辨率分支上学习到的全局特征传播扩散到高分辨率分支上以获取更强的语义信息。实验结果表明,该算法在数据集DNDS和MSRS上的分割精度优于现有语义分割算法,证明了提出算法的有效性。

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.