机械与电子2026，Vol.44Issue(1)：35-44,51,11.
基于生成对抗网络的红外与可见光视频语义特征驱动融合方法
Semantic Feature Driven Fusion Method for Infrared and Visible Videos Based on Generative Adversarial Networks
摘要
Abstract
To address the performance bottleneck in existing video fusion methods—which often priori-tize visual quality improvement while neglecting the requirements of downstream detection tasks and fail to achieve differentiated processing of target regions,a semantic feature-driven fusion method for infrared and visible light videos based on GAN is proposed.A semantic feature embedding module(SFEM)is de-signed to effectively integrate semantic features into the fusion network.This module calculates the simi-larity between source and target images in the feature representation space by using dynamically generated semantic-guided convolutional kernels and attention mechanisms.A dual-path feature extraction archi-tecture is constructed,comprising a texture feature extraction module(TFEM)and a contrast feature ex-traction module(CFEM).These modules separately extract contrast and detail information from infrared and visible light images,achieving deep fusion of texture,contrast,and semantic features,thereby enhan-cing downstream task performance.During the training phase,a target detection sub-module is integrat-ed.The detection loss is backpropagated into the fusion network,enabling task-oriented optimization of the fusion process.Experimental results demonstrate that this method outperforms other comparative ap-proaches in balancing pixel intensity and preserving target object texture,while also exhibiting significant advantages in downstream target detection tasks.关键词
双模态视频/语义特征/目标检测/深度学习Key words
bimodal video/semantic features/object detection/deep learning分类
信息技术与安全科学引用本文复制引用
兰海麟,吉琳娜,杨风暴,刘延东..基于生成对抗网络的红外与可见光视频语义特征驱动融合方法[J].机械与电子,2026,44(1):35-44,51,11.基金项目
山西省基础研究计划项目(202203021221104) （202203021221104）
中北大学研究生科技立项项目(20242029) （20242029）
