Simulation and optimization of imaging for specular surface defect detection system based on structured light field

Area of Science:

• Computer Vision
• Optical Engineering
• Materials Science

Background:

• High reflectivity of specular surfaces poses challenges for machine vision defect detection.
• Imaging quality is critical for accurate defect identification in industrial inspection.

Purpose of the Study:

• To enhance defect imaging on specular surfaces for improved machine vision inspection.
• To develop and optimize a defect detection system for challenging reflective materials.

Main Methods:

• Utilized structured lighting and light field cameras for defect capture.
• Developed an accurate and efficient imaging simulation system.
• Devised evaluation metrics and employed gradient descent for parameter optimization.

Main Results:

• Achieved optimal system imaging parameters for specular surfaces.
• Demonstrated the superiority of the optimized solution in defect detection.
• Correlated objective function values with defect image classification accuracy.

Conclusions:

• The proposed system effectively captures specular defects.
• Optimized imaging parameters significantly enhance defect detection performance.
• This approach offers a robust solution for inspecting reflective surfaces.