MultimodalGasData: Multimodal Dataset for Gas Detection and Classification

Abstract

The detection of gas leakages is a crucial aspect to be considered in the chemical industries, coal mines, home applications, etc. Early detection and identification of the type of gas is required to avoid damage to human lives and the environment. The MultimodalGasData presented in this paper is a novel collection of simultaneous data samples taken using seven different gas-detecting sensors and a thermal imaging camera. The low-cost sensors are generally less sensitive and less reliable; hence, they are unable to detect the gases from a longer distance. A thermal camera that can sense the temperature changes is also used while collecting the present multimodal dataset to overcome the drawback of using only the sensors for detecting gases. This multimodal dataset has a total of 6400 samples, including 1600 samples per class for smoke, perfume, a mixture of smoke and perfume, and a neutral environment. The dataset is helpful for the researchers and system developers to develop and train the state-of-the-art artificial intelligence models and systems.