An Application of Convolutional Neural Networks for Automatic Inspection

Automatic inspection in today´s manufacturing is critical to be competitive. In this paper, experimental results from the application of digital filters for defects detection in paper pulp production are shown. These filters have been automatically generated by means of a convolutional neural architecture, that uses a modified back-propagation algorithm. The main subjects discussed are: convolutional top-down spiral architecture, a tool used to automatically generate digital filters, a simple but effective modification to the back-propagation algorithm for this application, and experimental results