%0 Electronic Article %A Peng Wei %A Chang Liu %A Mengyuan Liu %A Yunlong Gao %A Hong Liu %K CNN-based reference comparison method %K convolutional neural network %K printed circuit board inspection %K PCB bare board defect detection methods %K PCB production process %K bare PCB defect classification algorithm %K reference comparison method %K digital image processing %K automatic optic inspection %X Printed circuit board (PCB) inspection is an essential part of PCB production process. Traditional PCB bare board defect detection methods have their own defects. However, the PCB bare board defect detection method based on automatic optic inspection is a feasible and effective method, and it is having more and more application in industry. Based on the idea of the reference comparison method, this study aims at studying the classification of defects. First of all, the method of extracting defect areas using morphology is studied; meanwhile, a data set containing 1818 images with 6 different detailed defect area image parts are produced. Then, in order to classify defects accurately, a traditional classification algorithm based on digital image processing was attempted, and a defect classification algorithm based on convolutional neural network was proposed. After experimental demonstration, in the actual results, the defect classification algorithm based on convolutional neural network can achieve a fairly high classification accuracy (95.7%), which is much higher than the traditional method, and the new method has stronger stability than the traditional one. %T CNN-based reference comparison method for classifying bare PCB defects %B The Journal of Engineering %D November 2018 %V 2018 %N 16 %P 1528-1533 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=b4satg83r31gh.x-iet-live-01content/journals/10.1049/joe.2018.8271 %G EN