Abstract
The defect detection of ceramic sanitary ware is often done manually, which is inefficient and unhealthy. The development of deep learning technology makes a non-contact and high-efficient method of inspection possible. In this paper, based on VGG-16 network, we proposed an optimized method for ceramic sanitary ware defect detection. We carried out pre-processing to denoise and enhance the original image data, improved activation function of MReLU, and used transfer learning method to train the model. The results of the test on the ceramic sanitary ware defection data set showed that the accuracy can reach 97.48%, which is 6.46% higher than that of the model using the ReLU activation function. The detection speed can reach 7 fps, which could meet the requirements of industrial online real-time production.
Sign-in/Register to access full text options 
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.
