电子科技2025，Vol.38Issue(9)：1-8,8.DOI:10.16180/j.cnki.issn1007-7820.2025.09.001
基于深度学习的红外与可见光图像匹配
Deep Learning-Based Image Matching of Infrared and Visible Image
摘要
Abstract
The image similarity detection algorithm based on CNN(Convolutional Neural Network)has poor a-bility to express image features and is only suitable for a single specific task,which is prone to overfitting risk.In this study,a method of DLIVM(Deep Learning-based Image Matching of Infrared and Visible Image)is proposed.This method uses BCN(Batch Channel Normalization),attention mechanism,metric learning and Frobenius norm to im-prove image matching performance and generalization ability.The ResNet-50(Residual Neural Network-50)net-work,which modified the BN(Batch Normalization)layer to BCN,is used as the backbone network to extract image features,and the attention mechanism is added inside the residual unit.The objective function is constructed by com-bining binary cross entropy loss and metric learning to improve the distinguishing ability of feature representation.The model parameters are regularized using the Frobenius norm to prevent overfitting.The results show that on three wide-ly used infrared and visible data sets,the accuracy of DLIVM method is improved by 3.30％,0.86％,2.00％,7.50％,1.50％and 0.69％,respectively,when compared with the comparison method.关键词
图像匹配/卷积神经网络/批通道归一化/异源图像匹配/注意力机制/度量学习/深度学习/模态不变性Key words
image matching/convolutional neural network/batch channel normalization/heterologous image matching/attention mechanism/metric learning/deep learning/modality invariance分类
信息技术与安全科学引用本文复制引用
熊子恒,张轩雄..基于深度学习的红外与可见光图像匹配[J].电子科技,2025,38(9):1-8,8.基金项目
国家自然科学基金(62276167)National Natural Science Foundation of China(62276167) （62276167）
