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Enhanced SSD framework for detecting defects in cigarette appearance using variational Bayesian inference under

Shichao Wu1, Xianzhou Lv2, Yingbo Liu1

  • 1School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China.

Mathematical Biosciences and Engineering : MBE
|March 8, 2024
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Summary

This study introduces an enhanced single-shot multibox detector (SSD) model to accurately detect minor cigarette defects, even with limited data. The improved model enhances detection accuracy and reduces computational needs in manufacturing.

Keywords:
SSDcigarette appearance defectstiny target detectionvariational Bayesian inference

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Quality Control

Background:

  • High-speed manufacturing faces challenges in detecting minor cosmetic defects due to sample scarcity.
  • Accurate defect detection is crucial for quality assurance in precision industries like cigarette manufacturing.

Purpose of the Study:

  • To develop an enhanced object detection model for improved identification of minor defects in cigarette manufacturing.
  • To address limitations of existing models in handling sporadic defect occurrences and limited sample datasets.

Main Methods:

  • Implementation of an enhanced single-shot multibox detector (SSD) model.
  • Integration of variational Bayesian inference for robust defect detection.
  • Inclusion of bounded intersection over union (BIoU) loss and advanced activation functions (ELU, Leaky ReLU).

Main Results:

  • The enhanced SSD300 and SSD512 models achieved up to a 1.2% increase in mean average precision (mAP) for small defect detection.
  • Ablation studies demonstrated a 1.5% mAP increase and a 5.92 GFLOPs reduction in computational requirements.
  • The model exhibited improved inference performance in scenarios with limited sample data.

Conclusions:

  • The proposed enhanced SSD model effectively improves the detection accuracy of minor defects in high-speed manufacturing.
  • The model's efficiency and robustness make it suitable for precision-oriented industrial applications with limited data.
  • This approach offers a viable solution for enhancing quality control in cigarette production.