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C-ViT: An Improved ViT Model for Multi-Label Classification of Bamboo Chopstick Defects

  • 0College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.
Sensors (basel, Switzerland) +

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February 13, 2026

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February 13, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces C-ViT, an improved Vision Transformer (ViT) model for bamboo chopstick defect detection. The model enhances quality inspection accuracy by incorporating a convolutional neural network feature extraction module and a novel Hard Examples Contrastive Loss function.

Area Of Science

  • Computer Vision
  • Machine Learning
  • Quality Control

Background

  • Disposable bamboo chopstick quality impacts consumer experience and safety.
  • Current quality inspection methods require refinement for production demands.
  • Vision Transformer (ViT) models face challenges with extreme aspect ratios, like bamboo chopsticks.

Purpose Of The Study

  • To develop an improved ViT model (C-ViT) for multi-label defect classification of bamboo chopsticks.
  • To introduce a novel Hard Examples Contrastive Loss (HCL) function to enhance learning from difficult examples.
  • To improve the accuracy and efficiency of bamboo chopstick quality inspection.

Main Methods

  • Proposed C-ViT model featuring a Convolutional Neural Network Feature Extraction (CFE) module to replace traditional patch embedding.
  • Developed Hard Examples Contrastive Loss (HCL) function for dynamic hard example selection and contrastive learning.
  • Evaluated models on a self-built bamboo chopstick defect dataset (BCDD) and the public VOC2012 dataset.

Main Results

  • C-ViT improved mean Average Precision (mAP) by 1.2% to 92.8% on the BCDD dataset compared to the ViTS model.
  • The addition of HCL further boosted performance to 94.3% mAP on the BCDD dataset.
  • The HCL function demonstrated effectiveness in multi-label classification tasks on the VOC2012 dataset.

Conclusions

  • The C-ViT model, with its CFE module, is effective for defect detection in objects with extreme aspect ratios.
  • The HCL function significantly enhances multi-label classification performance by focusing on hard examples.
  • The proposed methods offer a promising approach for improving automated quality inspection in manufacturing.

Keywords:

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