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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition

Okeke Stephen1, Samaneh Madanian1, Minh Nguyen1

  • 1Computer Science & Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

This study introduces an intelligent deep learning framework for industrial defect recognition, improving fault classification accuracy and efficiency. The novel voting policy enhances real-time visual inspection and quality control in manufacturing.

Keywords:
convolutional neural networksdeep learning ensembledefect recognition and classificationindustrial productsvisual inspectionvoting policy

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Traditional industrial inspection methods are complex, time-consuming, and error-prone, hindering effective quality control.
  • Existing intelligent models often sacrifice real-time performance for accuracy, limiting their practical application in manufacturing.

Purpose of the Study:

  • To develop an efficient, rapid, and intelligent model for industrial product fault recognition and classification.
  • To enhance visual inspection and quality control processes through improved defect detection.

Main Methods:

  • Proposed an ensemble deep learning framework utilizing a model architectural voting policy.
  • The voting policy considers model optimality, efficiency, and performance accuracy for feature learning.
  • Validated the framework on three publicly available industrial product datasets.

Main Results:

  • Demonstrated a significant increase in fault recognition and classification performance for industrial products.
  • The proposed model achieved remarkable results in identifying and categorizing defects.
  • The ensemble approach effectively computed and learned hierarchical features in industrial artifacts.

Conclusions:

  • The developed deep learning framework offers an efficient and accurate solution for industrial defect recognition.
  • The model's voting policy enhances performance, making it suitable for real-time quality control applications.
  • This approach significantly improves industrial product inspection and overall manufacturing quality.