Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

TL;DR

This paper presents a multimodal AI sensor fusion approach for gas detection and identification, achieving high accuracy by combining gas sensor data and thermal imaging, which outperforms single sensor methods.

Contribution

The paper introduces a novel multimodal AI fusion technique using gas sensors and thermal cameras for robust gas detection, with a new dataset of 6400 samples and an early fusion network architecture.

Findings

Fused model achieved 96% accuracy in gas identification.

Sensor fusion outperforms individual sensor models.

Thermal imaging enhances detection robustness.

Abstract

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.