Weld Defect Monitoring Based on Two-Stage Convolutional Neural Network  

作　　者：XIAO Wenbo XIONG Jiakai YU Lesheng HE Yinshui MA Guohong 肖文波;熊家凯;余乐盛;何银水;马国红

机构地区：[1]Key Laboratory of Nondestructive Testing Ministry of Education,Nanchang Hangkong University,Nanchang 330063,China [2]Key Laboratory of Lightweight and High Strength Structural Materials of Jiangxi Province,School of Advanced Manufacturing,Nanchang University,Nanchang 330031,China [3]School of Resources and Environment,Nanchang University,Nanchang 330031,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2025年第2期291-299,共9页上海交通大学学报(英文版)

基　　金：the National Natural Science Foundation of China(No.12064027)。

摘　　要：Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding process,then obtains laser fringe information through digital image processing,identifies welding defects,and finally realizes online control of weld defects.The performance of a convolutional neural network is related to its structure and the quality of the input image.The acquired original images are labeled with LabelMe,and repeated attempts are made to determine the appropriate filtering and edge detection image preprocessing methods.Two-stage convolutional neural networks with different structures are built on the Tensorflow deep learning framework,different thresholds of intersection over union are set,and deep learning methods are used to evaluate the collected original images and the preprocessed images separately.Compared with the test results,the comprehensive performance of the improved feature pyramid networks algorithm based on the basic network VGG16 is lower than that of the basic network Resnet101.Edge detection of the image will significantly improve the accuracy of the model.Adding blur will reduce the accuracy of the model slightly;however,the overall performance of the improved algorithm is still relatively good,which proves the stability of the algorithm.The self-developed software inspection system can be used for image preprocessing and defect recognition,which can be used to record the number and location of typical defects in continuous welds.

关 键 词：defects monitoring image preprocessing Resnet101 feature pyramid network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TG409[自动化与计算机技术—控制科学与工程]