Defect detection of ceramic substrates based on improved YOLOv9 algorithm

Key Points

• Defect detection accuracy increased with the improved YOLOv9 algorithm, indicating significant advancements in detection technology.
• The system achieved an accuracy of 92.5%, showcasing its effectiveness in identifying defects in ceramic substrates.
• Analysis employed a machine learning approach to refine the YOLO algorithm, enhancing its performance.
• This study may enable improved quality control practices in manufacturing ceramic materials.