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RJ-TinyViT: an efficient vision transformer for red jujube defect classification.

Chengyu Hu^1,2, Jianxin Guo^3,4, Hanfei Xie^1,2

¹School of Electronic Information, Xijing University, Xi 'an, 710123, China.

Scientific Reports

|November 13, 2024

Summary

This study introduces RJ-TinyViT, an optimized Tiny Vision Transformer (TinyViT), for red jujube surface defect detection. The model achieves higher accuracy with significantly reduced computational load, enhancing practical applications.

Keywords:

Coordinate attention Deep learning Red Jujube Surface defect detection Vision Transformer

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Computer Vision
Agricultural Technology
Machine Learning

Background:

Surface defect detection in naturally grown red jujubes is challenging due to high defect variability, low contrast, and noise.
Existing methods struggle with feature extraction and complex network structures, limiting efficiency and practical use.

Purpose of the Study:

To develop an efficient and accurate model for red jujube surface defect detection.
To address the limitations of current methods in feature extraction and network complexity.

Main Methods:

Proposed an optimized Tiny Vision Transformer (TinyViT) named RJ-TinyViT, refining the TinyViT-5m architecture.
Incorporated an improved Multi-Kernel Block (MK Block) and Mobile Inverted Bottleneck Convolution Block (MBConv Block) for enhanced feature extraction.
Integrated the Coordinate Attention (CA) module to improve focus on defect features.

Main Results:

RJ-TinyViT achieved a classification accuracy of 93.38%, an improvement of 1.84% over the original TinyViT.
Reduced Floating-point Operations (FLOPs) to 58.97% and Parameters (Params) to 39.84% of the original TinyViT network.
Demonstrated effective model lightweighting while maintaining high accuracy.

Conclusions:

RJ-TinyViT offers a practical solution for red jujube surface defect detection, balancing accuracy and efficiency.
The optimized network structure and integrated attention module enhance performance for agricultural product quality inspection.