Browse > Article
http://dx.doi.org/10.9717/kmms.2021.24.8.988

PCB Component Classification Algorithm Based on YOLO Network for PCB Inspection  

Yoon, HyungJo (Dept. of Computer Engineering, Graduate School, Keimyung University)
Lee, JoonJae (Faculty of Computer Engineering, Keimyung University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.8, 2021 , pp. 988-999 More about this Journal
Abstract
AOI (Automatic Optical Inspection) of PCB (Printed Circuit Board) is a very important step to guarantee the product performance. The process of registering components called teaching mode is first perform, and AOI is then carried out in a testing mode that checks defects, such as recognizing and comparing the component mounted on the PCB to the stored components. Since most of registration of the components on the PCB is done manually, it takes a lot of time and there are many problems caused by mistakes or misjudgement. In this paper, A components classifier is proposed using YOLO (You Only Look Once) v2's object detection model that can automatically register components in teaching modes to reduce dramatically time and mistakes. The network of YOLO is modified to classify small objects, and the number of anchor boxes was increased from 9 to 15 to classify various types and sizes. Experimental results show that the proposed method has a good performance with 99.86% accuracy.
Keywords
PCB Inspection; Defect Detection; AOI; Classification; Deep Learning; CNN;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J.H. Ryu, Y.H. Kim, and T.H. Park, "Assembly Defect Classification of SMD Components by Cascade Convolutional Neural Network," KIEE, Vol. 68, No. 10, 2019.
2 J.J. Lee, B.G. Lee and J.C. Yoo, "3-D Solder Paste Inspection Based on B-spline Surface Approximation," Journal of the Korean Society for Industrial and Applied Mathematics, Vol. 10, No. 1, 2006.
3 A. Fazakas, M. Purcar, and D. Turcu, "Polarity Determination of Electrolytic Capacitors in Power Supplies from External Terminals," Design and Technology in Electronic Packaging (SIITME) 2019 IEEE 25th International Symposium, pp. 395-398, 2019.
4 S.G. Youn, Y.A. Lee, and T.H. Park, "Automatic Classification of SMD Packages using Neural Network," Journal of Institute of Control, Robotics and Systems, Vol 21, No 3, pp. 276-282, 2015.   DOI
5 D.U. Lim, Y.G. Kim, and T.H. Park, "SMD Classification System on PCB Using Two Convolution Neural Networks," Journal of Institute of Control, Robotics and Systems, Vol. 25, No. 10, pp. 923-928, 2019.   DOI
6 Y.A. Lee, and T.H Park, "Automatic Recognition of SMD Packages Using Neural Network," The Korean Institute of Electrical Engineers, pp. 38-39, 2013.
7 S.M. Gang and J.J. Lee, "Coreset Construction for Character Recognition of PCB Components Based on Deep Learning," Journal of Korea Multimedia Society, Vol. 24, No 3, 382-395, 2021.03   DOI
8 U.H. Lee, Y.S. Choi, J.L. Kim, and G.S. Jung, "Generation of Reference Model for PCB Pattern Inspection from Gerber CAD Data," The Institute of Electronics and Information Engineers, pp. 317-323, 1994.
9 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016.
10 C.G. Cho and H.G. Park, "Main Parts Used in Electronic Ballast Diode, Transistor, PCB, Resistor," The Korean Institute of Illuminating and Electrical Installation Engineers, Vol. 13, No. 3, pp. 3-8, 1999.
11 J.S. Lee and T.H. Park, "Defect Classification of Components for SMT Inspection Machines," Journal of Institute of Control, Robotics and Systems, Vol. 21, No. 10, pp. 982-987, Oct. 2015.   DOI
12 L. Zhang, Y. Jin, X. Yang, X. Li, X. Duan, and Y. Sun, "Convolutional Neural Network-Based Multi-Label Classification of PCB defects," The Journal of Engineering, pp. 1612-1616, 2018.   DOI
13 N. Kavitha and D.N. Chandrappa, "Optimized YOLOv2 Based Vehicle Classification and Tracking for Intelligent Transportation System," Results in Control and Optimization, Vol. 2, 2021.
14 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, Vol. 1, pp. 91-99, 2015.
15 J. Sang, Z. Wu, P. Guo, H. Hu, xiang H, Q. Zhang, and B. Cai, "An Improved YOLOv2 for Vehicle Detection," Sensors, Vol. 18, No. 12, 2018.
16 L. Wang, W. Li, W. Zhang, and C. Wei, "Pedestrian Detection Based on YOLOv2 with Skip Structure in Underground Coal Mine," 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 1216-1220, 2017.
17 J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517-6525, 2017.
18 J. Redmon and A. Farhadi, "Yolov3: An Incremental Improvement," IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), 2018.
19 W Huang and P. Wei, "A PCB Dataset for Defects Detection and Classification," Journal of Latex Class Files, Vol. 14, No. 8, pp. 1-9, 2018.