检漏
泄漏
半导体
氧化物
气体泄漏
材料科学
人工智能
计算机科学
光电子学
工程类
化学
冶金
环境工程
有机化学
作者
Edmilson Sanches,Fábio Augusto,Paulo Márcio da Silveira,Bernardo Feijó Junqueira,Dimas Augusto Mendes Lemes,J.L. Picolo,Guilherme Ribeiro Sales,Valentino Corso,Cides S. Bezerra
出处
期刊:Revista de Informática Teórica e Aplicada
[Universidade Federal do Rio Grande do Sul]
日期:2025-02-20
卷期号:32 (1): 91-98
标识
DOI:10.22456/2175-2745.143526
摘要
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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