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With the rapid industrialization and technological advancements, innovativeengineering technologies which are cost effective, faster and easier toimplement are essential. One such area of concern is the rising number ofaccidents happening due to gas leaks at coal mines, chemical industries, homeappliances etc. In this paper we propose a novel approach to detect andidentify the gaseous emissions using the multimodal AI fusion techniques. Mostof the gases and their fumes are colorless, odorless, and tasteless, therebychallenging our normal human senses. Sensing based on a single sensor may notbe accurate, and sensor fusion is essential for robust and reliable detectionin several real-world applications. We manually collected 6400 gas samples(1600 samples per class for four classes) using two specific sensors: the7-semiconductor gas sensors array, and a thermal camera. The early fusionmethod of multimodal AI, is applied The network architecture consists of afeature extraction module for individual modality, which is then fused using amerged layer followed by a dense layer, which provides a single output foridentifying the gas. We obtained the testing accuracy of 96% (for fused model)as opposed to individual model accuracies of 82% (based on Gas Sensor datausing LSTM) and 93% (based on thermal images data using CNN model). Resultsdemonstrate that the fusion of multiple sensors and modalities outperforms theoutcome of a single sensor.

Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion