Statistics

    Map

Twitter


A Welding Defect Detection Algorithm Based on Deep Learning

( Vol-12,Issue-2,February 2025 ) OPEN ACCESS
Author(s):

Yi Chen, Yan Zuo Chang, Lin Po Shang, Ze Feng Lin, Yong Qi Chen, Liu Yi Yu, Wan Ying Wu, Jun Qi Liu

Keywords:

Deep learning, SCConv, Weld defect, YOLOv8

Abstract:

In order to meet the needs of process inspection technology for industrial equipment, image recognition technology based on deep learning has shown great potential in the field of welding defects. In this paper, an improved YOLOv8 algorithm is proposed to improve the welding defect identification ability of the workpiece. Through experimental verification on selected data sets in kaggle, this study evaluates the detection performance of YOLOv8 improved algorithm that integrates SCConv in C2f module at Backbone level. The experimental results show that the improved YOLOv8 has improved the accuracy of welding defect detection compared with the traditional version, and has certain application potential.

Article Info:

Received: 03 Jan 2025, Received in revised form: 08 Feb 2025, Accepted: 13 Feb 2025 Available online: 22 Feb 2025

ijaers doi crossref DOI:

10.22161/ijaers.122.4

Paper Statistics:
  • Total View : 1818
  • Downloads : 62
  • Page No: 31-38
Cite this Article:
Click here to get all Styles of Citation using DOI of the article.