Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks

被引：76

作者：

Adibhatla, Venkat Anil ^{[1
,2
]}

Chih, Huan-Chuang ^{[2
]}

Hsu, Chi-Chang ^{[2
]}

Cheng, Joseph ^{[2
]}

Abbod, Maysam F. ^{[3
]}

Shieh, Jiann-Shing ^{[1
]}

机构：

[1] Yuan Ze Univ, Dept Mech Engn, Taoyuan 32003, Taiwan

[2] Boardtek Elect Corp, Dept Adv Mfg Syst, Taoyuan 328454, Taiwan

[3] Brunel Univ London, Dept Elect & Comp Engn, Uxbridge UB8 3PH, Middx, England

来源：

ELECTRONICS | 2020年 / 9卷 / 09期

关键词：

convolution neural network; YOLO; deep learning; printed circuit board; DEPTH;

D O I：

10.3390/electronics9091547

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：16

共 34 条

[1] Anitha DB, 2017, 2017 2ND IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), P39, DOI 10.1109/WiSPNET.2017.8299715
[2] Representation Learning: A Review and New Perspectives
Bengio, Yoshua
Courville, Aaron
Vincent, Pascal
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (08) : 1798 - 1828
[3] Chauhan APS, 2011, LECT NOTES ENG COMP, P1597
[4] Cho H.S., 2012, U.S. Patent, Patent No. [8,159,824, 8159824]
[5] Convolutional Neural Network Committees For Handwritten Character Classification
Ciresan, Dan Claudiu
Meier, Ueli
Gambardella, Luca Maria
Schmidhuber, Juergen
[J]. 11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011), 2011, : 1135 - 1139
[6] Cong Jason, 2014, Artificial Neural Networks and Machine Learning - ICANN 2014. 24th International Conference on Artificial Neural Networks. Proceedings: LNCS 8681, P281, DOI 10.1007/978-3-319-11179-7_36
[7] Automated inspection of PCB components using a genetic algorithm template-matching approach
Crispin, A. J.
Rankov, V.
[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2007, 35 (3-4) : 293 - 300
[8] Devarakonda A., ARXIV2017171202029
[9] Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, Ross
Donahue, Jeff
Darrell, Trevor
Malik, Jitendra
[J]. 2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, : 580 - 587
[10] Hastie T., 2009, The Elements of Statistical Learning - Data Mining, Inference and Prediction, DOI [DOI 10.1007/978-0-387-84858-7, DOI 10.1007/B94608]

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