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A New Efficient Multi-Object Detection and Size Calculation for Blended Tobacco Shreds Using an Improved YOLOv7

Kunming Jia1, Qunfeng Niu1, Li Wang1

  • 1College of Electrical Engineering, Henan University of Technology, Zhengzhou 450000, China.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study introduces an improved YOLOv7-tiny model for detecting blended tobacco shreds and calculating their unbroken rate in cigarette manufacturing. The new model enhances accuracy for multi-object detection and size calculation, improving quality inspection.

Keywords:
LWCYOLOv7blended tobacco shredmulti-object detectionsize calculation

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Area of Science:

  • Computer Vision and Machine Learning
  • Industrial Automation and Quality Control

Background:

  • Accurate detection of tobacco shreds and calculation of unbroken rates are crucial for cigarette quality inspection.
  • Existing machine vision methods struggle with classifying tiny, morphologically complex, and overlapped tobacco shreds.

Purpose of the Study:

  • To develop an efficient multi-object detection model for blended tobacco shreds.
  • To accurately calculate the unbroken tobacco shred rate using a novel 2D size calculation algorithm.

Main Methods:

  • An improved YOLOv7-tiny model with a lightweight Resnet19 backbone was developed.
  • Key components like SPPCSPC and the detection head were replaced with SPPFCSPC and a decoupled joint detection head.
  • A novel Length-Width Calculation (LWC) algorithm was proposed for 2D size determination of individual shreds.

Main Results:

  • The model achieved high detection precision (0.883) and mAP (0.932@.5).
  • Testing time was significantly reduced to 4.12 ms.
  • Average length and width detection accuracy reached -1.7% and 13.2%, respectively, aligning with manual inspection standards.

Conclusions:

  • The developed model offers an efficient solution for multi-object detection and size calculation of blended tobacco shreds in cigarette production.
  • This approach provides a robust method for enhancing automated quality inspection in the tobacco industry and similar blended image analysis tasks.