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Updated: May 14, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
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Published on: July 25, 2025

Real-Time Lightweight Weld Seam Keypoint Detection and Tracking via an Improved SimCC with a Unified Three-Keypoint

Shenkuo Wang1, Xiangjie Huang2, Ang Gao1

  • 1School of Mechanical Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight framework for robust weld seam keypoint detection and tracking, significantly improving accuracy and speed in challenging industrial settings. The new method enhances robotic welding by providing reliable seam perception for precise control.

Keywords:
SimCCkeypoint detectionlightweight networklog-domain quadratic peak refinementsub-pixel localizationweld seam

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Artificial Intelligence

Background:

  • Industrial weld seam perception faces challenges due to arc light, spatter, smoke, and geometric variations, hindering accurate localization and real-time inference.
  • Existing methods struggle to provide a unified representation and robust tracking of weld seams under adverse conditions.

Purpose of the Study:

  • To develop an end-to-end lightweight framework for weld seam keypoint detection and tracking.
  • To address limitations in unified representation, accurate localization, and real-time inference for industrial welding.
  • To enable decoupled position and orientation control for robotic welding applications.

Main Methods:

  • An improved SimCC (Simplified Convolutional Network for Keypoint Detection) framework is utilized.
  • A unified three-keypoint formulation (center and two orientation points) is introduced for diverse weld geometries.
  • A lightweight C3k2-based backbone and a non-parametric log-domain quadratic peak-refinement decoder are employed to minimize quantization errors.

Main Results:

  • The proposed model achieves a low parameter count (1.4 M) and fast CPU inference latency (17.01 ms).
  • High detection accuracy is demonstrated with a Mean Absolute Error (MAE) of 1.89 pixels.
  • In curved weld seam tracking, the system achieved an average trajectory error of 0.159 mm and an average orientation error of 3.738°.

Conclusions:

  • The developed framework offers a robust, accurate, and real-time solution for weld seam perception in industrial environments.
  • The unified keypoint representation and refined decoder effectively handle geometric variations and reduce errors.
  • The system's performance validates its suitability for enhancing robotic welding precision and reliability.