Artificial Intelligence for Gas Leak Detection with Thermal Cameras and Metal Oxide Semiconductor Sensors
Early detection of gas leaks is crucial for safety and efficiency in oil platforms and refineries. The presence of various hazardous gases, often imperceptible to human senses, poses significant risks. AI-powered solutions can effectively monitor for gas leaks, improving safety and ensuring efficient operations. In this work we proposed a modular architecture effectively combines tabular data from gas sensors and spatial information from thermal images using a variety of backbones, including MobileNet. By employing dense layers and an optimized training strategy, we achieved state-of-the-art performance, with 100% accuracy, demonstrating the effectiveness of our approach for gas leakage detection.
Authors
- Bernardo Feijó Junqueira (ORCID: https://orcid.org/0000-0002-4692-9005)
- Dimas Augusto Mendes Lemes (ORCID: https://orcid.org/0000-0001-5189-4129)
- Paulo Márcio da Silveira (ORCID: https://orcid.org/0000-0002-6900-5745)
- Cides S. Bezerra (ORCID: https://orcid.org/0000-0002-3602-1909)
- J.L. Picolo
- Valentino Corso
- Edmilson Sanches
- Fábio Augusto
- Guilherme Ribeiro Sales
Institutions
- Centre for Research and Development in Telecommunications (Brazil) (BR)
- Pontifícia Universidade Católica de Campinas (BR)
Publication Details
- Journal
- Revista de Informática Teórica e Aplicada
- Published
- 2025-02-20
- DOI
- https://doi.org/10.22456/2175-2745.143526
- Primary Topic
- Gas Sensing Nanomaterials and Sensors
- Type
- article
- Field-Weighted Citation Impact
- 0.00