Multimodal Gas Detection Using E-Nose and Thermal Images:An Approach Utilizing SRGAN and Sparse Autoencoder  

作　　者：Pratik Jadhav Vuppala Adithya Sairam Niranjan Bhojane Abhyuday Singh Shilpa Gite Biswajeet Pradhan Mrinal Bachute Abdullah Alamri 

机构地区：[1]Artificial Intelligence and Machine Learning Department,Symbiosis Institute of Technology,Symbiosis International(Deemed)University,Pune,412115,India [2]Symbiosis Centre of Applied AI(SCAAI),Symbiosis International(Deemed)University,Pune,412115,India [3]Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),School of Civil and Environmental Engineering,University of Technology Sydney,Ultimo,NSW 2007,Australia [4]Department of Geology and Geophysics,College of Science,King Saud University,Riyadh,11362,Saudi Arabia

出　　处：《Computers, Materials & Continua》2025年第5期3493-3517,共25页计算机、材料和连续体(英文)

基　　金：funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney;supported by the Researchers Supporting Project,King Saud University,Riyadh,Saudi Arabia,under Project RSP2025 R14.

摘　　要：Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time realtime.Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems.The low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the images.The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal images.The dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature fusion.The Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal images.Asparse autoencoder was trained on the grayscale and colorized thermal images.The SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of 0.066.The autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of 0.0705.The multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,respectively.Hence,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.

关 键 词：Thermal imaging gas detection multimodal learning generative models autoencoders 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]