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An Improved Neural Network Model Based on DenseNet for Fabric Texture Recognition.

Li Tan1, Qiang Fu1, Jing Li1

  • 1College of Science & Technology, Ningbo University, Ningbo 315300, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary

This study introduces a novel Differentiated Leaning Weighted DenseNet (DLW-DenseNet) for automated knitted fabric texture recognition. The model enhances feature selection and achieves higher accuracy than existing methods.

Keywords:
DenseNetchannel attentiondeep learningdifferentiated learningfabric texture recognition

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Area of Science:

  • Computer Science
  • Materials Science
  • Textile Engineering

Background:

  • Automated knitted garment production requires accurate fabric texture identification for quality control.
  • Manual methods are inefficient and subjective; current machine learning approaches often rely on time-consuming manual feature extraction.
  • Existing methods face limitations in accuracy and efficiency for fabric texture recognition.

Purpose of the Study:

  • To develop a novel deep learning model for automated fabric texture recognition in knitted garments.
  • To address the limitations of manual feature extraction and improve recognition accuracy.
  • To introduce a new dataset for training and evaluating fabric texture recognition models.

Main Methods:

  • Introduced the Differentiated Leaning Weighted DenseNet (DLW-DenseNet), an enhanced DenseNet architecture.
  • Incorporated a learnable weight mechanism with channel attention to optimize feature selection and reduce redundancy.
  • Implemented a differentiated learning strategy with distinct learning rates for continuous channel selection and model pruning.
  • Constructed a new knitted fabric dataset named KF9.

Main Results:

  • The DLW-DenseNet model demonstrated superior performance in fabric texture recognition.
  • Achieved a five percentage point increase in recognition accuracy compared to an improved ResNet-based network.
  • Significantly outperformed other representative methods on the KF9 dataset.

Conclusions:

  • DLW-DenseNet effectively enhances automated fabric texture recognition in knitted garments.
  • The proposed model offers improved accuracy and efficiency over existing methods.
  • The KF9 dataset provides a valuable resource for future research in fabric texture recognition.