Related Experiment Video
Updated: Jan 16, 2026
05:47
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
Published on: August 29, 2025
423
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
Summary
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.
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.
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