金属加工（热加工）Issue(5)：49-55,62,8.
基于深度学习的承压设备焊接接头缺陷智能检测与评级
Deep learning-based intelligent detection and grading of weld joint defects in pressure equipment
许晓男 1乔通 2张玥 3宋鑫鑫 4马浩然5
作者信息
- 1. 北京安东软件技术有限公司 北京 100102
- 2. 安东石油技术(集团)有限公司 北京 100102
- 3. 北京通盛威尔工程技术有限公司 北京 100102
- 4. 北京数智启航科技有限公司 北京 101499
- 5. 北京安迅数智科技有限公司 北京 101400
- 折叠
摘要
Abstract
Aiming at the problems of strong subjectivity,low efficiency,and poor data traceability in the traditional manual film evaluation method for defects in welded joints of pressure equipment,an intelligent detection and rating method integrating deep learning target detection technology and the NB/T 47013 standard is proposed.A welding defect dataset is constructed and the YOLOv8s model is trained.The sliding window inference and coordinate mapping strategy are used to realize full-region defect recognition and localization of high-resolution films.Combined with the DPI pixel-to-physical size conversion and the rating rule engine adaptive to working condition parameters,the accurate calculation of defect parameters and standardized quality rating are completed,and a structured detection report is finally generated.Experimental results show that the method can effectively identify various defect types such as porosity,cracks,and incomplete penetration.The consistency between the rating results and the standard reference values of manual film evaluation reaches 96％,and the detection efficiency is improved by 4 times.It provides an efficient,objective,and traceable quality assurance scheme for the safe operation of pressure equipment.关键词
焊接接头/缺陷检测/深度学习/智能评级/NB/T47013标准Key words
welded joint/defect detection/deep learning/intelligent rating/NB/T 47013 standard引用本文复制引用
许晓男,乔通,张玥,宋鑫鑫,马浩然..基于深度学习的承压设备焊接接头缺陷智能检测与评级[J].金属加工（热加工）,2026,(5):49-55,62,8.
