Deep U-NET Based Heating Film Defect Inspection System

This study introduces a real-time, high-resolution image inspection system that utilizes multiple cameras and deep learning algorithms for the real-time detection of pinholes and scratches on large-area heating films. To accommodate the repetitive inspection processes inherent in products with consi...

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Bibliographic Details
Published in:International journal of precision engineering and manufacturing 2024-04, Vol.25 (4), p.759-771
Main Authors: Hwang, J. W., Park, H. J., Yi, H.
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Defects
Engineering
Heating
Image resolution
Industrial and Production Engineering
Inspection
Machine learning
Materials Science
Pinholes
Real time
Regular Paper
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Summary:This study introduces a real-time, high-resolution image inspection system that utilizes multiple cameras and deep learning algorithms for the real-time detection of pinholes and scratches on large-area heating films. To accommodate the repetitive inspection processes inherent in products with consistent patterns, the system operates at the region level rather than the frame level. By modifying the U-Net architecture, the system achieved precise segmentation of the inspection area, enabling real-time detection of microscale pinholes and scratches. Additionally, a sticker marker was developed to label the defective regions detected on the film. The proposed system was experimentally validated in an actual production environment, where it demonstrated an impressive 96.6% accuracy in area inspection and a 97.5% defect detection rate at a transportation speed of 12 m/min. These results serve as clear evidence of the effectiveness and practicality of the automatic detection capability facilitated by deep learning in production processes.
ISSN:2234-7593
2005-4602
DOI:10.1007/s12541-023-00937-x