基于深度学习算法的超声TOFD图像焊缝缺陷识别

Weld defect recognition in ultrasonic TOFD image based on depth learning algorithm

【目的】针对传统焊缝缺陷检测中人工效率低、检测难度大和漏检率高等问题,基于试验模拟与深度学习算法提出了一种面向TOFD D扫图像的缺陷识别方法。【方法】通过室内试验对不同类型的对接焊接缺陷(夹渣、裂纹、未焊透、气孔和未熔合)进行了模拟,利用不同数据增强方法构建了实际TOFD检测数据特征图库,结合YOLOv5深度学习网络结构进行TOFD D图像数据集的训练与检测,提升模型对焊接缺陷的识别性能,并且自动输出缺陷智能检测结果。【结果】试验结果表明,该方法具备优越的模型泛化能力,当IoU阈值设定为0.5时,模型训练的准确率可达到98.05%;针对5种焊接缺陷类型,在识别时的分类置信度均超过95%,有效提升了焊接缺陷识别的效率,可满足实际场景在线识别要求。【结论】提出的焊接缺陷识别方法具有较高的准确性,可广泛用于不同缺陷检测模型的构建,为焊接质量控制提供了有效的技术支持。

[Objective]Addressing the issues of low manual efficiency,high detection difficulty,and high missed detection rates in traditional weld defect detection,this study proposes a defect recognition method for TOFD D-scan images based on experimental simulation and deep learning algorithms.[Methods]Different types of butt weld defects(slag inclusions,cracks,lack of penetration,porosity,and lack of fusion)are simulated through indoor experiments.A feature image library of actual TOFD detection data is conducted by various data augmentation methods.TOFD D-scan image datasets are trained and detected by the YOLOv5 deep learning network structure to enhance the model’s capability to recognize weld defects and automatically output intelligent detection results.[Results]The experimental results indicate that this method possesses excellent model generalization ability,achieving an accuracy of 98.05%with an IoU threshold of 0.5.For five types of welding defects,the classification confidence during recognition exceeds 95%,significantly improving the efficiency of weld defect recognition and meeting the requirements for online recognition in practical scenarios.[Conclusion]The proposed weld defect recognition method demonstrates high accuracy and can be widely used for constructing various defect detection models,providing effective technical support for welding quality control.