Detection and Quantification of Methane Leaks

University of Nevada, Las Vegas

PI: Emma E. Regentova, Professor, Electrical and Computer Engineering 

Co-PI: Alexander Barzilov, Professor, Mechanical Engineering

Engineering Students

Abdulkarem Sennain (MSEE)
Oscar Salcido (CS)
Jared Rosario (CS) 
Antony Okeani (MSNE)
Dennis Weirich II (ME)
Jerome Ariola (ME)

Aurelia X6 MAX with Teledyne FLIR G300a camera and NVIDIA Jetson Nano

Presentation at UNLV’s Undergraduate Research Forum 

“ADVANCED ML-BASED ANALYSIS OF OPTICAL SENSOR DATA ON THE DRONE PLATFORM FOR ROBUST DETECTION OF METHANE LEAKS” is funded by Battelle Savannah River Alliance, LLC

The project is focused on the development of  airborne, autonomous (but human pilot monitored), real-time  methane leak detection technology that applies machine learning to passive optical sensor data with the goal of mitigating methane leak emissions through early detection.

Experiments with methane detection