深度学习在焊缝缺陷检测的应用研究综述

Summary of Research on Application of Deep Learning in Weld Defect Detection

焊缝缺陷的检测在石油化工等领域是极其关键的环节,焊接质量的好坏直接影响到结构的使用性能。对于X射线焊缝图像评定,目前采用的人工评片受到多种主观因素的影响,导致漏检或错检情况相对较高。近年来,随着工业智能检测技术的发展,深度学习在图像特征学习中的独特优势使其在缺陷自动检测中具备重要的实用价值。综述了以神经网络技术为代表的深度学习模型在焊缝缺陷检测方面的研究进展,详细分析了基于卷积神经网络和Faster R-CNN网络的工业设备焊缝缺陷自动检测的理论模型及其优缺点,并对焊缝缺陷自动检测技术的发展进行了展望。

The detection of weld defects is an extremely critical link in the petrochemical industry and other fields.The quality of the weld directly affects the performance of the structure.For the evaluation of X-ray weld image,the currently used manual evaluation is affected by a variety of subjective factors,resulting in a relatively high rate of missed or wrong inspections.In recent years,with the development of industrial intelligent detection technology,the unique advantages of deep learning in image feature learning make it have important practical value in automatic defect detection.The research progress of deep learning model represented by neural network technology in weld defect detection was summarized,and the theoretical models and their advantages and disadvantages of weld defect automatic detection based on convolutional neural network and Faster R-CNN network were analyzed in detail,and the development of automatic detection in weld defects was prospected.