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The Detection of Thread Roll's Margin Based on Computer Vision.

Zhiwei Shi¹, Weimin Shi¹, Junru Wang¹

¹School of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|October 13, 2021

Summary

This summary is machine-generated.

This study introduces a computer vision system using mobile robots for accurate thread roll margin detection. Fusing deep learning and Kalman filters significantly reduces errors, improving textile manufacturing precision.

Keywords:

Circle Gradient Operator Kalman Filter Keras computer vision deep learning detection of thread roll’s margin

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Area of Science:

Textile Manufacturing Technology
Computer Vision
Robotics

Background:

Traditional thread tension-based methods for detecting thread roll margins are costly and unreliable.
Accurate thread roll margin detection is crucial for efficient textile production.

Purpose of the Study:

To develop an automated, reliable, and precise method for detecting thread roll margins.
To overcome the limitations of traditional detection techniques in the textile industry.

Main Methods:

Utilizing a mobile robot equipped with a camera for image acquisition of the thread roll.
Applying computer vision techniques, including Circle Gradient Operator and ellipse fitting, for initial margin detection.
Implementing deep learning for enhanced radius detection and a Kalman Filter for fusing measurements and estimations.

Main Results:

Initial computer vision methods showed significant errors, especially with small margins (over 19.4 mm).
The combined deep learning and Kalman Filter approach reduced the average error to less than 5.7 mm.
The developed system demonstrates improved accuracy and reliability in thread roll margin detection.

Conclusions:

The proposed mobile robot and computer vision system, enhanced by deep learning and Kalman filtering, offers a superior solution for thread roll margin detection.
This technology has the potential to significantly improve automation and quality control in the textile industry.