Resistance Welding Spot Defect Detection with Convolutional Neural Networks

被引：18

作者：

Guo, Zhiye ^{[1
]}

Ye, Shaofeng ^{[1
]}

Wang, Yiju ^{[1
]}

Lin, Chun ^{[1
]}

机构：

[1] Xiamen Univ, Sch Aerosp Engn, Xiamen 361005, Peoples R China

来源：

COMPUTER VISION SYSTEMS, ICVS 2017 | 2017年 / 10528卷

基金：

中国国家自然科学基金;

关键词：

Welding spot; Defect detection; Convolutional neural networks;

D O I：

10.1007/978-3-319-68345-4_15

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A convolutional neural network based method is proposed in this paper to classify the images of resistance welding spot. The features of resistance wielding spots are very complex and diverse, which made it difficult to separate the good ones and the bad ones using hard threshold. Several types of convolutional neural networks with different depths and layer nodes are built to learn the features of welding spot. 10 thousand labeled images are used for training and 3 hundred images are used to test the network. As a result, we get a 99.01% accuracy on test images, which is 97.70% better than human inspection.

引用

页码：169 / 174

页数：6

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