A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

Kai Ding, Zhangqi Niu, Jizhuang Hui, Xueliang Zhou and Felix T. S. Chan ()
Additional contact information
Kai Ding: Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China
Zhangqi Niu: Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China
Jizhuang Hui: Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China
Xueliang Zhou: School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
Felix T. S. Chan: Department of Decision Sciences, Macau University of Science and Technology, Macao SAR, China

Mathematics, 2022, vol. 10, issue 19, 1-18

Abstract: Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.

Keywords: weld surface; defect recognition; MobileNetV2; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/19/3678/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/19/3678/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:19:p:3678-:d:936037

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().