Enhancement of Product-Inspection Accuracy Using Convolutional Neural Network and Laplacian Filter to Automate Industrial Manufacturing Processes

Abstract

The automation of the manufacturing process of printed circuit boards (PCBs) requires accurate PCB inspections, which in turn require clear images that accurately represent the product PCBs. However, if low-quality images are captured during the involved image-capturing process, accurate PCB inspections cannot be guaranteed. Therefore, this study proposes a method to effectively detect defective images for PCB inspection. This method involves using a convolutional neural network (CNN) and a Laplacian filter to achieve a higher accuracy of the classification of the obtained images as normal and defective images than that obtained using existing methods, with the results showing an improvement of 11.87%. Notably, the classification accuracy obtained using both a CNN and Laplacian filter is higher than that obtained using only CNNs. Furthermore, applying the proposed method to images of computer components other than PCBs results in a 5.2% increase in classification accuracy compared with only using CNNs.