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深度神经网络用于工业地面储中的外部腐蚀分类
Anibal Alviz-Meza1, Leidy L Hadechini-Meza2, Darío Y Peña-Ballesteros3
1Grupo de Investigación en Arquitectura, Diseño e Ingeniería (GIADI), Facultad de Ingeniería y Arquitectura, Institución Universitaria Mayor de Cartagena, Cartagena, Colombia.
Heliyon
|February 17, 2025
概括
本研究介绍了一种人工智能工具,用于使用EfficientNet模型检测储中的腐蚀类型. 微调的EfficientNetB0实现了94%的准确性,提高了工业水箱检查的安全性.
科学领域:
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
背景情况:
- 在地表储中的腐蚀对石油和天然气行业构成重大风险.
- 准确和早期检测腐蚀类型对于运营安全和资产完整性至关重要.
研究的目的:
- 开发和评估一种自动化视觉检查工具,用于分类储中的腐蚀类型.
- 探索转移学习和微调的有效性,使用EfficientNet模型来完成这项任务.
主要方法:
- 在EfficientNet的较小模型上利用转移学习和微调.
- 在大约5000张图像的数据集中应用数据增强.
- 在Google Colaboratory中使用TensorFlow和Keras训练模型.
主要成果:
- 微调的EfficientNetB0模型在腐蚀类型的多重分类中实现了94%的性能.
- 这代表了人工视觉工具首次用于工业水箱检查的部署.
结论:
- 开发的人工视觉工具在识别腐蚀类型方面表现出很高的准确性.
- 未来与物体检测的整合可以实现对外部腐蚀故障的远程识别,提高石油和天然气运营中的安全性和可靠性.
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