电焊机2025，Vol.55Issue(5)：30-36,54,8.DOI:10.7512/j.issn.1001-2303.2025.05.04
基于改进YOLOv5网络的管道环焊缝射线底片缺陷智能识别
Pipeline Girth Weld Defect Intelligent Identification of Digital Radio-graph based on an Improved YOLOv5 Network
摘要
Abstract
Pipeline girth welds are a crucial component of oil and gas pipelines,significantly impacting the safety of natural gas and other transmissions.Currently,X-ray Digital Radiography(DR)technology has become an essential method for the quality inspection of girth welds.However,existing visual inspection methods struggle to achieve high-precision defect iden-tification.Therefore,this paper proposes a deep learning-based algorithm for defect detection in pipeline girth welds using DR images.Firstly,to address issues such as complex workpiece defect structures and noise and scatter introduced by X-rays,guided filtering methods are used for data enhancement,and a pipeline girth weld DR dataset is created.Secondly,the backbone network of the improved YOLOv5 model,combined with the C2F(CSP Bottleneck with 2 convolutions)module,is employed to learn the defect feature distributions.The multi-scale feature fusion detection method is combined to judge the target defects of different sizes.The mean square error loss function is further used to improve the tolerance of noise,and the automatic detection of weld defects is realized through the output end.Finally,the common defects of pipeline girth welds were tested and verified,and the results showed that the average accuracy of detection results by the improved YO-LOv5 deep network model proposed in this paper was 95.89%,and the missed detection rate was 2.1%,which could effec-tively and accurately automatically identify weld defects in DR Images.关键词
管道环焊缝/YOLOv5/深度学习/缺陷检测/DR图像Key words
pipeline girth welds/YOLOv5/deep learning/defect detection/DR image分类
矿业与冶金引用本文复制引用
汤大赟,袁健伟,吴頔,何东昌,张培磊,潘斌,龚浩,金鸿飞..基于改进YOLOv5网络的管道环焊缝射线底片缺陷智能识别[J].电焊机,2025,55(5):30-36,54,8.基金项目
国家自然科学基金(52075317) （52075317）
江苏省特种设备安全监督检验研究院科技计划项目(KJ(Y)202418) （KJ(Y）
上海市Ⅲ类高峰学科—材料科学与工程(高能束智能加工与绿色制造) （高能束智能加工与绿色制造）
