基于改进的VGG-16网络结构的焊缝缺陷识别技术研究

Research on Weld Defect Recognition Technology Based on Improved VGG-16 Network Structure

焊接技术在多个领域广泛应用,近年来焊缝缺陷的自动检测已成为研究的热点。本文针对铝合金熔焊焊缝的X射线图像,采用VGG-16卷积神经网络作为基础网络,提出了一种SC-VGG的新型网络结构。该结构通过引入多尺度合成卷积层来替代传统的单一卷积层,优化了训练过程中的损失函数,使网络更加聚集于焊缝缺陷类型的精确预测。实验结果表明,SC-VGG网络结构在训练过程中展现出了良好的收敛性。与其他网络相比,SC-VGG网络在提取焊缝缺陷特征方面表现优异,其平均准确率和召回率分别达到了95.86%和98.33%,为焊缝缺陷的自动化分类提供了算法支撑。

Welding technology is widely used in multiple fields,and the automatic detection of weld defects has become a research hotspot in recent years.In this paper,aiming at the X-ray images of aluminum alloy fusion welding seams,a new network structure called SC-VGG is proposed,using the VGG-16 convolutional neural network as the basic network.This structure replaces the traditional single convolutional layer with a multi-scale synthetic convolutional layer and optimizes the loss function in the training process,making the network more focused on accurate prediction of weld defect types.Experimental results show that the SC-VGG network structure exhibits good convergence during the training process.Compared with other networks,the SC-VGG network performs excellently in extracting weld defect features,with an average accuracy and recall rate reaching 95.86%and 98.33%respectively,providing algorithm support for the automatic classification of weld defects.