Binarization of Printed Wiring Board Images with Scratches Using Convolutional Neural Network with U-Net Structure

Abstract

This paper presents the method by which binarize Printed Wiring Board(PWB) images. Multilayer printed wired boards have many wiring layers inside the board, and the images capturing these wiring layers contains a large number of scratches and stains, because the wiring layer images are captured while the surface is thinly scraped. We aim to recover the wiring pattern as a binarized image from such images. We have devised an input layer based on the U-Net structure, which consists of 1) 3-channel RGB input (conventional U-Net), 2) 5-channel input by adding L and S channels of HLS color space to RGB, 3) 4-channel input by adding a binary image by Otsu's method of L to RGB, 4) a loop that repeats the structure of method 3), and compared them. As a result, we found that method 3) with Otsu's binarization added to the input was superior in all the indices of accuracy, F-measure, mIoU, and the number of breaks and short circuits.