摘要
基于中俄东线管道射线检测底片图像和数据,采用Faster R-CNN、YOLO等深度学习算法,建立了全自动焊接环焊缝射线检测缺陷样本数据库,完成了未熔合等主要缺陷类型智能识别技术的研究和开发,初步实现了未熔合、裂纹等危害性缺陷的智能识别。在中俄东线智慧管道建设的目标框架下,射线检测底片图像识别等人工智能新技术的开发和应用,有助于实现管道大数据价值的充分挖掘,提升管道智能化运营管理的水平。
Using deep learning network of Faster R-CNN and YOLO, we performed defect recognition for radiographic image of China-Russia Eastern Gas pipeline. In this study, the defect sample database of automatic welding girth welds was established, and the research of defect recognition for radiographic image was completed, which preliminarily realized the intelligent identification of hazardous defects such as lack-of-fusion and cracks. Under the construction target of China-Russia Eastern Gas Pipeline, the development and application of new artificial intelligence technologies such as radiographic image recognition can help to unearth the value of pipeline big data and improve the level of pipeline intelligent operation and management.
作者
雷铮强
王维斌
李立军
LEI Zhengqiang;WANG Weibin;LI Lijun(PipeChina General Research Institute of Science and Technology,Langfang 065099,China;Langfang Branch of Hebei Special Equipment Supervision and Inspection Institute,Langfang 065001,China)
出处
《无损检测》
CAS
2022年第4期73-78,共6页
Nondestructive Testing
基金
国家管网科学研究与技术开发项目(WZXGL202107,JCGL202109)。
关键词
油气管道
射线检测图像
环焊缝
深度学习
oil and gas pipeline
radiographic testing image
girth weld
deep learning
