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Related Experiment Video

Updated: Feb 2, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

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Learning Siamese networks for laser vision seam tracking.

Yanbiao Zou, Jinchao Li, Xiangzhi Chen

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|November 22, 2018

Summary

This study introduces a deep learning algorithm using Siamese networks for stable laser vision seam tracking. The method effectively resists welding interference, enabling precise real-time tracking in metal inert-gas welding.

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Area of Science:

Robotics and Automation
Computer Vision
Materials Science

Background:

Welding processes, particularly metal inert-gas welding, face challenges with arc and spatter interference affecting seam tracking accuracy.
Traditional seam-tracking systems struggle with real-time adaptation to dynamic welding environments.
The need for robust and precise laser vision seam tracking is critical for automated welding quality.

Purpose of the Study:

To develop an advanced weld image processing algorithm for stable laser vision seam-tracking.
To enhance the resilience of seam-tracking systems against arc and spatter interference.
To achieve real-time and precise tracking of welding seams in challenging conditions.

Main Methods:

Investigated and proposed a deep learning-based algorithm utilizing Siamese networks for weld image processing.
The Siamese network accepts two differently sized welding images as input.
Employed a cross-correlation algorithm to generate a target confidence map and an online update strategy via local cosine similarity to prevent error accumulation and model drift.

Main Results:

The proposed Siamese network-based algorithm demonstrates robust feature expression capabilities.
The system successfully generates a target confidence map in a single forward pass.
An online update strategy effectively mitigates error accumulation and model drift.
Real-time and precise tracking was achieved during metal inert-gas welding, even with continuous arc interference.

Conclusions:

The developed Siamese network-based algorithm provides a stable and advanced solution for laser vision seam tracking.
The system's ability to resist arc and spatter interference enhances its practical applicability in industrial welding.
This approach offers a significant improvement for real-time, precise seam tracking in demanding welding environments.