With the rapid industrialization and technological advancements, innovative
engineering technologies which are cost effective, faster and easier to
implement are essential. One such area of concern is the rising number of
accidents happening due to gas leaks at coal mines, chemical industries, home
appliances etc. In this paper we propose a novel approach to detect and
identify the gaseous emissions using the multimodal AI fusion techniques. Most
of the gases and their fumes are colorless, odorless, and tasteless, thereby
challenging our normal human senses. Sensing based on a single sensor may not
be accurate, and sensor fusion is essential for robust and reliable detection
in several real-world applications. We manually collected 6400 gas samples
(1600 samples per class for four classes) using two specific sensors: the
7-semiconductor gas sensors array, and a thermal camera. The early fusion
method of multimodal AI, is applied The network architecture consists of a
feature extraction module for individual modality, which is then fused using a
merged layer followed by a dense layer, which provides a single output for
identifying the gas. We obtained the testing accuracy of 96% (for fused model)
as opposed to individual model accuracies of 82% (based on Gas Sensor data
using LSTM) and 93% (based on thermal images data using CNN model). Results
demonstrate that the fusion of multiple sensors and modalities outperforms the
outcome of a single sensor.Comment: 14 Pages, 9 Figure