Abstract
Visual inspection for liquid pouches based on contamination extraction and conventional deep learning model is under discussion. Pictures of the produced liquid pouches were taken using soft X-ray transmission to generate deep learning datasets. In the pre-processing, a convolutional autoencoder was trained with defect-free datasets and performed for contamination extraction. In addition, the conventional deep learning model was trained with pre-processed defect-free and defective datasets and performed for defect detection. In our experiments, we compared systems using each of the seven conventional deep learning methods and clearly demonstrated their performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
