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Real-Time Seam Extraction Using Laser Vision Sensing: Hybrid Approach with Dynamic ROI and Optimized RANSAC.

Guojun Chen^1,2, Yanduo Zhang^1,2, Yuming Ai^1,2

¹School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel real-time weld seam extraction method using laser vision sensors. It effectively overcomes arc light and spatter interference for accurate weld tracking.

Keywords:

dynamic ROI laser vision optimized RANSAC weld seam extraction

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Area of Science:

Robotics and Automation
Computer Vision
Manufacturing Technology

Background:

Laser vision sensors are crucial for automated welding but struggle with arc light and spatter.
Existing methods lack robustness in dynamic welding environments.

Purpose of the Study:

To develop a real-time weld seam extraction method robust to welding interference.
To enhance the accuracy and reliability of laser vision systems in industrial welding.

Main Methods:

Sequential processing of historical frame data for robustness.
Dynamic region of interest (ROI) generation to suppress interference.
Adaptive Otsu thresholding, morphological filtering, and optimized RANSAC with slope constraints for laser stripe fitting.

Main Results:

Accurate extraction of laser stripe and weld seam center coordinates.
Significant suppression of arc light and spatter interference.
Validated real-time performance and accuracy across various weld seam types.

Conclusions:

The proposed method offers a robust and accurate solution for real-time weld seam extraction.
It significantly improves the performance of laser vision sensors in challenging welding environments.
Enables more reliable automated welding processes.