Open Access

ARTICLE

Defect Detection in Printed Circuit Boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

Mohammed A. Alghassab^*

Electrical Engineering Department, College of Engineering, Shaqra University, Riyadh, 11911, Saudi Arabia

* Corresponding Author: Mohammed A. Alghassab. Email:

(This article belongs to the Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)

Computers, Materials & Continua 2022, 70(1), 637-652. https://doi.org/10.32604/cmc.2022.019527

Received 15 April 2021; Accepted 16 May 2021; Issue published 07 September 2021

Abstract

Printed Circuit Boards (PCBs) are very important for proper functioning of any electronic device. PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs. If PCBs do not function properly then the whole electric machine might fail. So, keeping this in mind researchers are working in this field to develop error free PCBs. Initially these PCBs were examined by the human beings manually, but the human error did not give good results as sometime defected PCBs were categorized as non-defective. So, researchers and experts transformed this manual traditional examination to automated systems. Further to this research image processing and computer vision came into actions where the computer vision experts applied image processing techniques to extract the defects. But, this also did not yield good results. So, to further explore this area Machine Learning and Artificial Intelligence Techniques were applied. In this study we have applied Deep Neural Networks to detect the defects in the PCBS. Pretrained VGG16 and Inception networks were applied to extract the relevant features. DeepPCB dataset was used in this study, it has 1500 pairs of both defected and non-defected images. Image pre-processing and data augmentation techniques were applied to increase the training set. Convolution neural networks were applied to classify the test data. The results were compared with state-of-the art technique and it proved that the proposed methodology outperformed it. Performance evaluation metrics were applied to evaluate the proposed methodology. Precision 94.11%, Recall 89.23%, F-Measure 91.91%, and Accuracy 92.67%.

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Keywords

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