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CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects.

Yihai Ma1,2, Guowu Yuan1,2, Kun Yue1,2

  • 1School of Information Science and Engineering, Yunnan University, Kunming 650504, China.

Mathematical Biosciences and Engineering : MBE
|December 5, 2023
PubMed
Summary

A new defect detection model, CJS-YOLOv5n, enhances cigarette quality control on high-speed production lines. This advanced You Only Look Once Version 5 Nano model significantly improves accuracy and speed for defect identification.

Keywords:
C2FJump ConcatSIoUYOLOv5ncigarettedefect detection

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Area of Science:

  • Industrial automation
  • Computer vision
  • Quality control

Background:

  • Cigarette production lines face challenges with appearance defects impacting product quality.
  • Existing defect detection methods struggle to balance accuracy and speed for high-throughput manufacturing.

Purpose of the Study:

  • To develop an accurate and fast defect detection model for cigarette appearance on production lines.
  • To address the limitations of current methods in achieving high detection rates per second.

Main Methods:

  • Proposed CJS-YOLOv5n model, integrating YOLOv8's C2F module, Jump Concat, and SIoU loss function.
  • Utilized YOLOv5n (You Only Look Once Version 5 Nano) as the base architecture.
  • Implemented enhancements for improved feature extraction, minor defect preservation, and localization accuracy.

Main Results:

  • CJS-YOLOv5n achieved a detection speed exceeding 500 frames per second (FPS).
  • The model increased recall rate by 2.3% and mean average precision (mAP)@0.5 by 1.7%.
  • Demonstrated superior performance suitable for high-speed cigarette production lines.

Conclusions:

  • The CJS-YOLOv5n model offers a significant advancement in automated cigarette appearance defect detection.
  • The model effectively balances high-speed processing with enhanced detection accuracy.
  • This technology is well-suited for real-time quality control in the tobacco industry.