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.

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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
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article

Artificial Intelligence for Gas Leak Detection with Thermal Cameras and Metal Oxide Semiconductor Sensors

Bernardo Feijó Junqueira, Dimas Augusto Mendes Lemes, Paulo Márcio da Silveira, Cides S. Bezerra et al.
1 links
Revista de Informática Teórica e Aplicada
Gas Sensing Nanomaterials and Sensors
article

Artificial Intelligence for Gas Leak Detection with Thermal Cameras and Metal Oxide Semiconductor Sensors

Bernardo Feijó Junqueira, Dimas Augusto Mendes Lemes, Paulo Márcio da Silveira, Cides S. Bezerra, J.L. Picolo, Valentino Corso, Edmilson Sanches, Fábio Augusto, Guilherme Ribeiro Sales
article en

Abstract

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.

Revista de Informática Teórica e AplicadaVol. 32(1)
Centre for Research and Development in Telecommunications (Brazil) (BR), Pontifícia Universidade Católica de Campinas (BR)
Openalex Percentile: Top 98%
Gas Sensing Nanomaterials and Sensors
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