Artificial Intelligence for Gas Leak Detection with Thermal Cameras and Metal Oxide Semiconductor Sensors - Sanches, Edmilson; Augusto, Fábio; Silveira, Pauline; Junqueira, Bernardo Feijó; Lemes, Dimas A. M.; Picolo, José; Sales, Guilherme Ribeiro; Corso, Valentino; Bezerra, Cides S. (Anais do Workshop de Visão Computacional (WVC); 2024: Anais do XIX Workshop de Visão Computacional, 2024)
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.