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Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT

Li Wang1, Kunming Jia1, Yongmin Fu2

  • 1School of Electrical Engineering, Henan University of Technology, Zhengzhou, ;China.

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Summary

This study introduces an improved Mask RCNN model for accurately classifying overlapping tobacco shreds and calculating their areas. The new method enhances identification accuracy for complex tobacco shred images, improving quality control.

Keywords:
CotMask RCNNarea computationinstance segmentationoverlapped tobacco shred

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Agricultural Technology

Background:

  • Accurate classification and component area calculation of tobacco shreds are crucial for determining blending ratios and overall quality.
  • Complex physical characteristics and overlapping/stacking of tobacco shreds, especially expanded tobacco silk and tobacco silk, pose significant challenges for traditional machine vision systems.
  • Existing methods struggle with distinguishing similar varieties and accurately calculating areas in overlapped tobacco shred images.

Purpose of the Study:

  • To develop a novel segmentation model for identifying various types of overlapping tobacco shreds.
  • To accurately calculate the areas of these overlapping tobacco shreds.
  • To address the limitations of current machine vision techniques in tobacco shred analysis.

Main Methods:

  • An improved Mask region-based convolutional neural network (RCNN) was developed, replacing its backbone with Densenet121 and U-FPN.
  • Region proposal network (RPN) parameters (anchor size and aspect ratios) were optimized for better feature extraction.
  • A novel algorithm for calculating the overlapped tobacco shred region (COT) area was proposed and applied to segmented images.

Main Results:

  • The developed model achieved a final segmentation accuracy of 89.1% and a recall rate of 73.2%.
  • The average area detection rate for 24 overlapped tobacco shred samples improved significantly from 81.2% to 90%.
  • The study demonstrated high accuracy in both segmentation and overlapped area calculation for tobacco shreds.

Conclusions:

  • The proposed improved Mask RCNN model offers an effective solution for the type identification and component area calculation of overlapped tobacco shreds.
  • This research provides a new methodological approach for similar image segmentation tasks involving complex, overlapped objects.
  • The findings contribute to advancing automated quality inspection systems in the tobacco industry.