基于深度学习的承压设备焊接接头缺陷智能检测与评级OA

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.