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Online fabric defect inspection using smart visual sensors.

Yundong Li¹, Jingxuan Ai, Changqing Sun

¹School of Information Engineering, North China University of Technology, Beijing 100041, China. liyundong@ncut.edu.cn

Sensors (Basel, Switzerland)

|April 11, 2013

Summary

This study introduces an automated fabric inspection system for warp knitting machines using smart visual sensors. The novel system achieves a 98% detection rate for fabric defects, enhancing textile quality control.

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Area of Science:

Textile Engineering
Computer Vision
Image Processing

Background:

Manual fabric inspection is labor-intensive and prone to errors.
Automated inspection systems are crucial for improving efficiency and quality in textile manufacturing.
Warp knitting machines require specialized defect detection methods.

Purpose of the Study:

To develop and evaluate a novel automatic inspection scheme for warp knitting machines.
To improve the efficiency and accuracy of fabric defect detection.
To address challenges in identifying broken-end defects.

Main Methods:

Implementation of a system with multiple smart visual sensors and a controller.
Utilization of high-definition image sensors and Digital Signal Processing (DSP) processors.

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Application of wavelet transform for image decomposition and an improved direct thresholding method.

Employing mathematical morphology filters for noise reduction and optimized algorithms for real-time defect detection.

Main Results:

The proposed system effectively detects fabric defects on a warp knitting machine.
The automated inspection system achieved a high detection rate of 98%.
The system demonstrated effectiveness during a six-month operational trial in a factory setting.

Conclusions:

The developed smart visual sensor system offers an effective solution for automated fabric defect inspection.
The system significantly enhances quality control in the textile industry by improving detection efficiency and accuracy.
The proposed method is suitable for real-time defect detection in warp knitting processes.