A Comparative Analysis of Early Fusion Architectures for Multimodal Gas Detection Using Machine Learning Models

Single-sensor gas detection models often lack robustness and accuracy, hindering safety and security.To enhance the accurate classification performance data from seven sensors along with thermal camera images has been used in this study, to train the model.The dataset focuses on four classes: Smoke, Perfume, No Gas and Mixture of Smoke and Perfume.Data from various sources capture different perspectives that enhance classification of the trained model, hence, early fusion technique was adopted to combine the extracted features, for an improved feature space.The sensor data undergoes preprocessing to normalize and remove noise.VGG16 model was used to extract image features.The fused data then acted as an input for the machine learning models for classification Among the tested models (SVM, Random Forest Classifier, and KNN), the Random Forest model achieved the best validation accuracy of 96.41%, outperforming SVM (94.22%) and KNN (94.53%).This approach demonstrates the effectiveness of multi-sensor data fusion for enhanced gas detection with high accuracy, potentially improving response times and reducing false alarms.