Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21596
Title: Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks
Authors: Adibhatla, VA
Chih, H-C
Hsu, C-C
Cheng, J
Abbod, M
Shieh, JS
Keywords: convolution neural network;YOLO;deep learning;printed circuit board
Issue Date: 22-Sep-2020
Publisher: MDPI
Citation: Adibhatla, V.A.; Chih, H.-C.; Hsu, C.-C.; Cheng, J.; Abbod, M.F.; Shieh, J.-S. Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks. Electronics 2020, 9, 1547, pp. 1-16. doi: 10.3390/electronics9091547.
Abstract: © 2020 by the authors. In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32.
URI: https://bura.brunel.ac.uk/handle/2438/21596
DOI: https://doi.org/10.3390/electronics9091547
Other Identifiers: 1547
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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