Analysis of Training Deep Learning Models for PCB Defect Detection.

Area of Science:

• Computer Vision
• Machine Learning
• Manufacturing Technology

Background:

• Automated defect detection is crucial for defect-free printed circuit board (PCB) manufacturing.
• Deep learning-based image understanding methods are increasingly prevalent in industrial applications.
• Variations in industrial image data, including contamination and quality degradation, pose challenges for automated systems.

Purpose of the Study:

• To analyze the training of deep learning models for stable PCB defect detection.
• To identify and analyze factors affecting industrial image data quality.
• To provide comprehensive knowledge and guidelines for accurate PCB defect detection.

Main Methods:

• Summarized characteristics of industrial images, specifically PCB images.
• Analyzed factors causing data changes like contamination and quality degradation.
• Organized and reviewed various defect detection methods applicable to PCB inspection.
• Conducted experiments to evaluate the impact of degradation factors on detection performance.

Main Results:

• Demonstrated the significant impact of data quality and image contamination on deep learning model performance for PCB defect detection.
• Highlighted the influence of different defect detection methods under varying image conditions.
• Quantified the effects of common industrial image degradation factors.

Conclusions:

• Established the importance of robust data preprocessing and model selection for reliable PCB defect detection.
• Provided practical guidelines for implementing stable and accurate automated inspection systems.
• Emphasized the need to consider image quality and contamination in deep learning-based manufacturing quality control.