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Image synthesis of apparel stitching defects using deep convolutional generative adversarial networks.

Noor Ul-Huda¹, Haseeb Ahmad¹, Ameen Banjar²

¹Department of Computer Science, National Textile University, Faisalabad, Pakistan.

|February 29, 2024

Summary

This study introduces a Deep Convolutional Generative Adversarial Network (DCGAN) to automatically generate realistic fabric stitching defects. This addresses data scarcity, improving deep learning models for industrial quality control.

Keywords:

Deep convolutional generative adversarial network Deep learning Defect generation Image synthesis

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Area of Science:

Industrial Manufacturing
Computer Vision
Artificial Intelligence

Background:

Accurate detection of fabric stitching defects is crucial for industrial quality control.
Deep learning models excel at defect detection but require extensive training data, which is often scarce in practical settings due to collaboration and privacy issues.

Purpose of the Study:

To develop an automated method for generating realistic fabric stitching defects.
To overcome the challenge of limited training data for deep learning-based fabric defect detection.

Main Methods:

Utilized a Deep Convolutional Generative Adversarial Network (DCGAN) for synthetic defect generation.
Conducted quantitative and qualitative assessments, including expert validation and Fréchet Inception Distance (FID) analysis.

Main Results:

Generated synthetic stitching defects with high accuracy, validated by ten industrial experts achieving 80% accuracy.
Fréchet Inception Distance scores indicated promising results for the generated data realism.

Conclusions:

The proposed DCGAN-based defect generation model effectively produces realistic stitching defective data.
This approach successfully bridges the data scarcity gap in practical industrial fabric defect detection, enhancing quality control.