Weld Defect Detection based on Improved Multi-Scale CNN with CBAM Attention-Reference-Cited by-同舟云学术

Weld Defect Detection based on Improved Multi-Scale CNN with CBAM Attention

Published:2023-07-14 Issue: Volume: Page:
ISSN:
Container-title:2023 6th International Conference on Signal Processing and Machine Learning (SPML)
language:
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Author:

Huang Lei¹^ORCID,Zhang Shanwen²^ORCID,Li Rujiang³^ORCID,Cai Xuming⁴^ORCID,Zhang Shengguang⁵^ORCID,Cao Huayong⁶^ORCID

Affiliation:

1. CNPC Tubular Goods Research Institute, China

2. College of Electronic Information, XiJing University, China

3. North China Petroleum Steel Pipe Co., Ltd, China

4. Shashi Steel Works SINPEC Oilfield Equipment Corporation, China

5. Shandong Shengli Steel Pipe Co.,Ltd, China

6. Nanjing Julong Steel Pipe Co.,Ltd, China

Funder

The National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3614008.3614042

Reference19 articles.

1. Automatic classification approach to weld defects based on PCA and SVM

2. Mu W Liu G Peng L A novel classification approach of weld defects based on dual-parameters optimization of PCA and LDA . International Conference on Advances in Mechanical Engineering & Industrial Informatics. 2015 : 1425 - 1429 . Mu W Liu G Peng L A novel classification approach of weld defects based on dual-parameters optimization of PCA and LDA. International Conference on Advances in Mechanical Engineering & Industrial Informatics. 2015:1425-1429.

3. X-Ray Testing of Weld Defect of Automatic Recognit Ion and Alarm Technology Research

4. Yazid H , Arof H , Yazid H , Weld Detect Identification Using Texture Features and Dynamic Time Warping. Applied Mechanics & Materials , 2015 , 752-753:1045-1050. Yazid H, Arof H, Yazid H, Weld Detect Identification Using Texture Features and Dynamic Time Warping. Applied Mechanics & Materials, 2015, 752-753:1045-1050.

5. Welding defect classification based on convolution neural network (CNN) and Gaussian kernel