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Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification.

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Summary
This summary is machine-generated.

A new machine vision framework accurately detects and classifies ski goggle lens defects. This automated system achieves high accuracy, ensuring quality for manufacturers and consumers.

Keywords:
HOSVDadaptive energy analysisautomatic optical inspectionparallel projection in opposite directionsski goggles lens

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Area of Science:

  • Manufacturing Technology
  • Computer Vision
  • Quality Control

Background:

  • Ski goggles are essential protective gear, but manufacturing defects on lenses are common.
  • Manual visual inspection for ski goggle lens defects is challenging and error-prone.
  • Automated defect detection is crucial for maintaining high-quality ski goggle production.

Purpose of the Study:

  • To develop and validate a novel machine vision framework for automated ski goggle lens defect detection and classification.
  • To improve the efficiency and accuracy of quality control in ski goggle manufacturing.
  • To address the limitations of manual inspection in identifying subtle lens surface defects.

Main Methods:

  • A machine vision framework utilizing five high-resolution cameras and custom lighting was developed.
  • Defect detection employed parallel projection with adaptive energy analysis.
  • Defect image enhancement used adaptive high-order singular value decomposition (HOSVD).
  • A classification algorithm identified six defect types: dust, spotlight (3 types), string, and watermark.

Main Results:

  • The framework achieved 100% accurate defect detection rate.
  • The classification accuracy rate reached 99.3% for six defect types.
  • The system demonstrated a short total running time, suitable for industrial application.
  • Validation was performed on 120 ski goggle lens samples from a major manufacturer.

Conclusions:

  • The proposed machine vision framework is effective for ski goggle lens defect inspection.
  • The automated system significantly enhances quality control accuracy and efficiency in manufacturing.
  • This method provides a sound and useful solution for the ski goggles industry.