深度学习方法用于在天然气管道中检测钻孔腐蚀
Ivan Malashin1, Vadim Tynchenko1, Vladimir Nelyub1,2
1Artificial Intelligence Technology Scientific and Education Center, Department of Welding, Diagnostics and Special Robotics, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了一种计算机视觉方法,使用自定义卷积神经网络 (CNN) 来检测天然气管道中的蚀腐蚀. 深度学习方法实现了高精度,取代了手动检查.
科学领域:
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 在天然气管道中,坑道腐蚀带来了重大风险.
- 手动检查腐蚀是劳动密集型和昂贵的.
- 先进的检测方法对于基础设施完整性至关重要.
研究的目的:
- 开发一种自动化计算机视觉方法,用于检测天然气管道中的蚀.
- 为腐蚀分类创建一个高效和准确的深度学习模型.
- 为了减少对手动管道检查的依赖.
主要方法:
- 策划了576,000个管道图像的大数据集.
- 设计和优化了一个定制的卷积神经网络 (CNN) 用于二进制分类.
- 利用深度学习进行图像分析和腐蚀检测.
主要成果:
- 实现了98.44%的高分类准确度,用于检测穿孔腐蚀.
- 与当代分类器相比,定制的CNN模型表现出卓越的性能.
- 该方法有效地区分了腐蚀和非腐蚀的管道图像.
结论:
- 拟议的计算机视觉和深度学习方法为天然气管道中自动检测孔腐蚀提供了有效的解决方案.
- 这种方法显著简化了检查流程,减少了时间和成本.
- 该CNN模型的高精度验证了其在管道完整性管理中的现实应用潜力.
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