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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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An Integrated and Robust Vision System for Internal and External Thread Defect Detection with Adversarial Defense.

Liu Fu1, Leqi Li1, Gengpei Zhang1

  • 1The School of Electronic Information and Electrical Engineering, Yangtze University, East Campus, Jingzhou 434100, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a robust vision system for industrial thread defect detection. It uses advanced imaging, generative data augmentation, and a lightweight deep learning model for accurate and secure inspection.

Keywords:
YOLO-based optimizationalpha channel attackimage data augmentationlightweight neural networkthread defect detection

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Area of Science:

  • Industrial Automation
  • Computer Vision
  • Machine Learning

Background:

  • Detecting defects in threaded components is difficult due to complex geometry and hidden micro-flaws.
  • Existing methods struggle with robustness, limited data, and adversarial attacks.

Purpose of the Study:

  • To develop an integrated, robust vision system for inspecting internal and external threads.
  • To enhance defect detection accuracy and efficiency using advanced AI techniques.
  • To ensure system reliability against adversarial perturbations.

Main Methods:

  • A unified imaging platform for synchronized thread surface capture.
  • Advanced image enhancement for motion blur and low-light conditions.
  • Generative data augmentation to address limited defect samples.
  • A lightweight, optimized deep learning model for defect detection.
  • A dual-defense mechanism against adversarial attacks.

Main Results:

  • The system achieves high robustness in inspecting both internal and external threads.
  • Advanced techniques improve image clarity and overcome data limitations.
  • The optimized deep learning model surpasses existing YOLO variants in precision and efficiency.
  • The dual-defense mechanism effectively mitigates adversarial perturbations, ensuring reliability.

Conclusions:

  • The proposed framework offers accurate, secure, and efficient thread defect detection.
  • This provides a practical solution for reliable industrial vision systems.
  • The integration of advanced imaging and AI enhances quality control in manufacturing.