Natural Gas Leakage Detection: a Deep Learning Framework on IR Video Data

• Main Authors: Spatafora, Maria Ausilia Napoli, Allegra, Dario, Giudice, Oliver, Stanco, Filippo, Battiato, Sebastiano
• Format: Conference Proceeding
• Language: English
• Subjects: Deep learning; Optical computing; Optical detectors; Optical imaging; Optical losses; Recurrent neural networks; Stimulated emission
• ISSN: 2831-7475
• DOI: 10.1109/ICPR56361.2022.9956523

Undetected gas leakages may result in serious fire and explosion accidents with consequences like injuries among workers and financial losses. Automated leak detectors aimed to catch in time the gas emissions could reduce the incident risks. Several monitoring techniques have been developed over the years, among them the Optical Gas Imaging (OGI) is a widely-used method but it typically requires manual analysis (slow and error-prone). This paper introduces an automated gas leakage detection framework exploiting Infrared video data. A novel Recurrent Neural Network architecture was designed and trained on an ad-hoc collected large-scale dataset. Experimental results demonstrated the effectiveness of the proposed framework outperforming the state-of-the-art approaches with an average accuracy of 98%. The robustness of the technique was also validated in different scenarios and with different camera settings.