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Updated: Jan 16, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
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Research on steel structure weld seam recognition algorithm based on improved YOLOv5.

Shijie Zhu1, Lixin Zhang2, Jiawei Zhao3

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 83200, Xinjiang, China.

Scientific Reports
|October 1, 2025
PubMed
Summary

This study introduces an improved YOLOv5 algorithm for steel structure weld detection, enhancing accuracy by integrating Coordinate Attention (CA) to overcome background interference. The new method boosts weld recognition performance while reducing computational load.

Keywords:
Attention mechanismSteel structureWeld identificationYOLOv5

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Structural Engineering

Background:

  • Steel structure weld recognition faces challenges from complex backgrounds and low detection accuracy.
  • Existing methods struggle with precise localization and identification of welds in industrial settings.

Purpose of the Study:

  • To develop an enhanced YOLOv5 target detection algorithm for improved steel structure weld recognition.
  • To increase the accuracy and efficiency of weld detection in complex industrial environments.

Main Methods:

  • An improved YOLOv5 algorithm was proposed, incorporating the Coordinate Attention (CA) mechanism.
  • A novel C3CA module was designed by integrating CA into the C3 structure of the backbone network.
  • The algorithm was trained and evaluated on a self-made dataset of steel structure welds.

Main Results:

  • The improved YOLOv5s-C3CA model achieved a mean Average Precision (mAP@0.5) of 93.79%, a 2.48% increase over the original model.
  • Model parameters were reduced by 8.53%, and floating-point operations decreased by 10.6%.
  • The study demonstrated a balance between enhanced detection accuracy and computational efficiency.

Conclusions:

  • The Coordinate Attention mechanism effectively improves the feature expression and spatial perception capabilities of the YOLOv5 model for weld detection.
  • The proposed C3CA module offers a viable solution for automatic weld detection in industrial applications, addressing accuracy and efficiency concerns.