USING DEEP LEARNING FOR AUTOMATIC DEFECT DETECTION ON A SMALL WELD X-RAY IMAGE DATASET

被引：0

作者：

Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin ^{[1
]}

300384, China

不详 ^{[2
]}

机构：

来源：

UPB Sci. Bull. Ser. C Electr. Eng. Comput. Sci. | / 2卷 / 267-278期

基金：

中国国家自然科学基金;

关键词：

Convolutional neural networks - Deep neural networks - Generative adversarial networks - Welding - Welds;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The quality of welding is directly related to the performance and life of welded products. This paper proposes an automatic defect detection method using deep learning on a small weld X-ray image dataset. Combined with Generative Adversarial Network (GAN) and Deep Convolutional Neural Network (DCNN), this method can successfully deal with the problem of data imbalance in small image dataset and achieves a good detection effect for low-contrast defect images. Extensive experiments have proved that this approach could accurately and quickly complete the location and detection task of internal defects of welds, and it achieves the Mean Average Precision (mAP) result as 91.64%. © 2022, Politechnica University of Bucharest. All rights reserved.

引用

页码：267 / 278