An ANN–based embedded hardware for gas leak detection using a SWIR imaging system

Abstract

This paper suggests a new short wave infrared (SWIR)-imaging technique which can overcome these limitations. In addition to a two dimensional (2D) SWIR camera, the system also comprises a 2D visible light camera, an Inertial Measurement Unit (IMU), and global positioning system (GPS) to accurately determine the location of the leak using image correlation and triangulation techniques. The paper also suggests a low cost experimental setup used to assess the performance of the system to accurately quantify and localize CH4 gas leak. An Artificial Neural Network (ANN), was assessed using this setup. Series of extensive experimental tests demonstrate the capability of the system to detect, quantify, and localize CH4 gas leak for different scenarios. The corresponding results reveal that the ANN algorithm yields accurate results for gas mass leak measurement and localization using a SWIR optical filter. Uncertainties of gas mass leak flow measurement did not exceed 2.1% and 3.76% using a SWIR LED source with and without a SWIR filter respectively. This leads to state that the suggested system can be a tangible alternative for next generation leak detection systems.