一种双分支网络结构的典型电气设备多源图像融合算法

聂启新; 肖志云; 鲍腾飞; 靳旭; 高文强; 郭浩

聂启新,肖志云,鲍腾飞,靳旭,高文强,郭浩.一种双分支网络结构的典型电气设备多源图像融合算法[J].电测与仪表,2025,62(5):68-75.
nieqixin,xiaozhiyun,baotengfei,jinxu,gaowenqiang,guohao.A multi-source image fusion algorithm for typical electrical equipment with dual-branch network structure[J].Electrical Measurement & Instrumentation,2025,62(5):68-75.

一种双分支网络结构的典型电气设备多源图像融合算法

A multi-source image fusion algorithm for typical electrical equipment with dual-branch network structure

DOI：10.19753/j.issn1001-1390.2025.05.008

中文关键词: 图像融合技术双分支网络电气设备可见光图像和红外图像图像配准深度学习

英文关键词:image fusion technology, dual-branch network, visible light image and infrared image of electrical equipment, image registration, deep learning

基金项目:国家自然科学基金（61661042）；内蒙古自治区科技计划项目（2021GG0345）；内蒙古自治区自然科学基金项目（2021MS06020）

作者中文名	作者英文名	单位
聂启新	nieqixin	内蒙古工业大学电力学院
肖志云	xiaozhiyun	内蒙古工业大学电力学院
鲍腾飞	baotengfei	内蒙古工业大学电力学院
靳旭	jinxu	内蒙古工业大学电力学院
高文强	gaowenqiang	内蒙古工业大学电力学院
郭浩	guohao	内蒙古工业大学电力学院

摘要点击次数: 93

中文摘要:

随着智能电网系统的快速发展，为提升热故障的准确定位，图像融合技术得到了广泛的关注。文中以变电站电气设备可见光和红外图像为研究对象，通过深度学习方法设计网络模型，以自动编码器为主干网络，其中编码器采用设计的密集连接分支和加强分支双分支网络结构，一个分支为密集连接分支，使用密集块连接和自注意力机制来提取边缘和细节特征，另一个分支为加强分支，采用改进的特征金字塔结构（feature pyramid network,FPN），增强全局信息。文中通过双分支结构得到两组相应特征，采用L1-范数融合策略将特征进行融合后输入解码器重建融合图像。经过与多种方法对比，文中方法从主观视觉评价、客观图像融合评价指标两方面验证了该算法的先进性，其中客观评价指标QMI、SSIM、FMIpixel分别为0.567 26、0.593 47、0.887 60，达到最高值，证明融合图像质量得到提升，适用于电气设备多源图像融合。

英文摘要:

With the rapid development of smart grid systems, image fusion technology has received wide attention in order to improve the accurate location of thermal faults. In this paper, the visible and infrared images of substation electrical equipment are used as the research object, and the network model is designed by deep learning method with auto-encoder as the backbone network, in which the encoder adopts the designed dual-branch network structure of densely connected branch and enhanced branch, one branch is densely connected branch using dense block connection and self-attention mechanism to extract edge and detail features, and the other branch is enhanced branch using a strengthened branch with an improved feature pyramid network (FPN) structure to enhance the global information. In this paper, two sets of corresponding features are obtained by the dual-branch structure, and the features are fused by the L1-parametric fusion strategy and input the decoder to reconstruct the fused image. After comparing with various methods, the method in the paper verifies the advancement of the algorithm in terms of subjective visual evaluation and objective image fusion evaluation indices, in which the objective evaluation indices QMI, SSIM and FMIpixel are 0.567 26, 0.593 47 and 0.887 60, respectively, reaching the highest value, which proves that the quality of fused images is improved and applicable to multi-source electrical equipment image fusion.

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