Defect And Components Recognition In Printed Circuit Boards Using Convolution Neural Network - Cheong, Leong Kean (Universiti Sains Malaysia, 2018)
The growth of electronic devices increases the demands of printed circuit boards (PCB) productions in the electronic industries. This leads to the rise in the quantity of PCB productions every day. Consequently, automated visual inspection becomes an essential system to be equipped in any production line to ensure the quality of the PCB produced which brings us to the aim of this project, building an automated components recognition system for PCB using CNN. In addition to that, localization on the defects of the PCB components will also be performed. In the first stage, a simple CNN-based component recognition classifier will be developed. Since training a CNN from scratch is expensive, transfer learning with ImageNet pre-trained models is performed instead. Pre-trained models such as VGG16, DenseNet169 and InceptionV3 are used to investigate which model suits the best for components recognition. Using transfer learning with VGG-16, the best result achieved is 99% accuracy with the capability of recognizing up to 25 different components. Following that, object localization is performed using faster region-based convolutional neural network (R-CNN). Multiple experiments have been performed to determine the optimum method and training parameters to achieve a system that is able to localize defects on the PCB with high accuracy and precision. The best mean average precision (mAP) achieved for the defects localization system is 96.54%.