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Industrial Weld Defect Detection Based on Monocular Depth Estimation and Dual-Attention Point Cloud Network.

Nannan Zhao1, Shijie Chen2

  • 1School of Computer Science and Engineering, Guangdong Ocean University, Yangjiang 529568, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

This study introduces a low-cost framework for detecting weld defects using monocular depth estimation and a dual-attention point cloud network. The method achieves high accuracy and recall, offering a cost-effective solution for industrial automation.

Keywords:
PointNet++dual attention mechanismindustrial quality inspectionmonocular depth estimationpseudo-point cloudweld defect detection

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Area of Science:

  • Industrial Quality Control
  • Computer Vision
  • Machine Learning

Background:

  • Traditional 2D methods for weld defect detection suffer from illumination and texture interference.
  • High-precision 3D laser scanning is expensive and impractical for large-scale industrial use.
  • Accurate geometric defect identification is crucial for industrial quality control.

Purpose of the Study:

  • To develop a cost-effective framework for reliable geometric weld defect detection.
  • To address challenges in identifying complex missed weld defects with distinct spatial features.
  • To enhance feature representation in point cloud networks for improved classification.

Main Methods:

  • Utilized YOLOv8 for rapid region of interest extraction.
  • Employed advanced monocular depth estimation to generate 3D pseudo-point clouds.
  • Introduced a dual-attention enhanced point cloud classification network (DA-PointNet++) integrating dual-attention modules into PointNet++.

Main Results:

  • Achieved 93.67% accuracy and 90.51% recall in a unified binary classification task.
  • Effectively identified both normal welds and complex missed weld defects.
  • Demonstrated significant reduction in false negative rates compared to PointConv, DGCNN, and Point Cloud Transformer.

Conclusions:

  • The proposed framework offers a cost-effective solution for industrial automation in weld defect detection.
  • DA-PointNet++ enhances feature representation for improved geometric defect identification.
  • The approach provides reliable and low-cost geometric defect detection, overcoming limitations of existing methods.