计算机工程与应用2024，Vol.60Issue(14)：228-239,12.DOI:10.3778/j.issn.1002-8331.2304-0288
双分支GAN与注意力机制的火灾隐患检测算法
Fire Hazard Detection Algorithm with Dual-Branch GAN and Attention Mechanism
摘要
Abstract
Aiming at the problems of traditional fire alarm can not be warned before the occurrence of fire,the effect is not good in extreme weather such as night,and it is limited by complex environment,a fire alarm algorithm based on infrared and visible light images fusion is proposed.A two-branch attention structure is designed and proposed in a genera-tive adversarial network(GAN).One branch extracts more robust feature information through the dense residual network,and the other branch makes up for the lack of spatial information through the efficient coordinate channel attention group(ECCAG)to maximize the acquisition of more high-frequency detail features,and designs and proposes a regulation loss as a loss function,and obtains the fusion image by improving the GAN algorithm.Finally,according to the proposed fire warning algorithm,whether there is a fire hazard is judged.The experimental results show that,the average accuracy of object detection in the fusion dataset obtained by the improved GAN algorithm is 96.19％,which is improved by 11.09 percentage points and 6.2 percentage points compared with the infrared dataset and the original GAN algorithm dataset,respectively,and the accuracy of flame hazard detection on the TNO and LLVIP datasets of the public dataset is 97.45％.The results show that the fire warning algorithm can warn in time when no fire occurs,and can obtain significant detec-tion effects for different scenarios.关键词
生成对抗网络/图像融合/早期火灾预警/双分支结构/注意力机制Key words
generative adversarial network/image fusion/early fire warning/two-branched structure/attention mechanism分类
信息技术与安全科学引用本文复制引用
李牧,何金诚,杨恒..双分支GAN与注意力机制的火灾隐患检测算法[J].计算机工程与应用,2024,60(14):228-239,12.基金项目
陕西省教育厅科研计划项目(18JK0341) （18JK0341）
西安市科技计划项目(2020KJRC0083). （2020KJRC0083）
