Method for Color-ring Resistor Detection and Localization in Printed Circuit Board Based on Convolutional Neural Network

Xiaoyan LIU; Zhaoming LI; Jiaxu DUAN; Tianyuan XIANG

doi:10.11999/JEIT190608

Volume 42 Issue 9

Sep. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(9): 2302-2311

Xiaoyan LIU, Zhaoming LI, Jiaxu DUAN, Tianyuan XIANG. Method for Color-ring Resistor Detection and Localization in Printed Circuit Board Based on Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2302-2311. doi: 10.11999/JEIT190608

Citation:

Xiaoyan LIU, Zhaoming LI, Jiaxu DUAN, Tianyuan XIANG. Method for Color-ring Resistor Detection and Localization in Printed Circuit Board Based on Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2302-2311. doi: 10.11999/JEIT190608

Citation:

Xiaoyan LIU, Zhaoming LI, Jiaxu DUAN, Tianyuan XIANG. Method for Color-ring Resistor Detection and Localization in Printed Circuit Board Based on Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2302-2311. doi: 10.11999/JEIT190608

PDF( 4673 KB)

Method for Color-ring Resistor Detection and Localization in Printed Circuit Board Based on Convolutional Neural Network

doi: 10.11999/JEIT190608

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2.
Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Changsha 410082, China
3.
Areospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Funds: The National Natural Fundation of China (61973108, U1913202), The Open fund for Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Industry (IRT2018001)

Received Date: 2019-08-09
Rev Recd Date: 2020-05-26

Available Online: 2020-06-23

Publish Date: 2020-09-27

Abstract

Abstract

The color-ring resistor is one of the most commonly used electronic components in Printed Circuit Board (PCB). It is featured by sequential color rings, which often brings assembling errors, however. Manual detection of color-ring resistors has low efficiency and high false detection rate. Traditional image-based automatic detection methods have difficulties in dealing with PCB images under various illuminations, imaging distance and views. To solve this problem, an automatic detection and localization method for PCB color-ring resistor is proposed based on Convolution Neural Network (CNN). Firstly, the encoder-decoder CNN model is established and trained using weighted cross-entropy loss function. With CNN, color-ring resistors are segmented from PCB images with complex illumination and scenes. Secondly, each color-ring resistor is localized using minimum area bounding rectangle, and its position is adjusted to the vertical direction by affine transformation. Finally, the localization of color rings on the resistor is achieved by Gaussian template matching. The proposed method is tested and verified by 1270 PCB images, and the result is compared with that of the traditional method (method based on geometric contour, and method based on template matching). It is shown that the proposed method has obvious advantages in performance indices, including recall rate, precision, and intersection of unions, which can meet the requirements of practical applications.
- Image segmentation,
- Color-ring resistor,
- Convolutional Neural Network(CNN),
- Printed Circuit Board (PCB)

FullText(HTML)

References(21)

References

熊光洁, 马树元, 聂学俊, 等. 基于机器视觉的高密度电路板缺陷检测系统[J]. 计算机测量与控制, 2011, 19(8): 1824–1826.

XIONG Guangjie, MA Shuyuan, NIE Xuejun, et al. Defects inspection system of HID PCB based on machine vision[J]. Computer Measurement &Control, 2011, 19(8): 1824–1826.

吴福培, 张宪民. 印刷电路板无铅焊点假焊的检测[J]. 光学精密工程, 2011, 19(3): 697–702. doi: 10.3788/OPE.20111903.0697

WU Fupei and ZHANG Xianmin. Inspection of pseudo solders for lead-free solder joints in PCBs[J]. Optics and Precision Engineering, 2011, 19(3): 697–702. doi: 10.3788/OPE.20111903.0697

CHEN Y S and WANG J Y. Reading resistor based on image processing[C]. 2015 IEEE International Conference on Machine Learning and Cybernetics, Guangzhou, China, 2015: 566–571. doi: 10.1109/ICMLC.2015.7340616.

GAIDHANE V H, HOTE Y V, and SINGH V. An efficient similarity measure approach for PCB surface defect detection[J]. Pattern Analysis and Applications, 2018, 21(1): 277–289. doi: 10.1007/s10044-017-0640-9

倪尧, 鲍宇. 基于目标轮廓几何特征的电容元件定位方法[J]. 计算机工程与科学, 2017, 39(8): 1476–1482. doi: 10.3969/j.issn.1007-130X.2017.08.014

NI Yao and BAO Yu. A capacitor element localization method based on geometrical features of target contour[J]. Computer Engineering and Science, 2017, 39(8): 1476–1482. doi: 10.3969/j.issn.1007-130X.2017.08.014

DONG Na, WU C H, IP W H, et al. Chaotic species based particle swarm optimization algorithms and its application in PCB components detection[J]. Expert Systems with Applications, 2012, 39(16): 12501–12511. doi: 10.1016/j.eswa.2012.04.063

王耀南, 刘良江, 周博文, 等. 一种基于混沌优化算法的PCB板元件检测方法[J]. 仪器仪表学报, 2010, 31(2): 410–415. doi: 10.19650/j.cnki.cjsi.2010.02.028

WANG Yaonan, LIU Liangjiang, ZHOU Bowen, et al. Detection method of printed circuit board components based on chaotic optimization algorithm[J]. Chinese Journal of Scientific Instrument, 2010, 31(2): 410–415. doi: 10.19650/j.cnki.cjsi.2010.02.028

姜建国, 王国林, 孟宏伟, 等. 一种电子元器件组装结果检测方法[J]. 西安电子科技大学学报: 自然科学版, 2014, 41(3): 110–115, 173. doi: 10.3969/j.issn.1001-2400.2014.03.016

JIANG Jianguo, WANG Guolin, MENG Hongwei, et al. Detection method for assembling results of electronic components[J]. Journal of Xidian University, 2014, 41(3): 110–115, 173. doi: 10.3969/j.issn.1001-2400.2014.03.016

毛林威. 轴向色环电阻质量自动检测系统的设计[D]. [硕士论文], 北京理工大学, 2015.

MAO Linwei. The design of color-ring resistor quality automatic detection system[D]. [Master dissertation], Beijing Institute of Technology, 2015.

CHEN Y S and WANG J Y. Computer vision on color-band resistor and its cost-effective diffuse light source design[J]. Journal of Electronic Imaging, 2016, 25(6): 061409. doi: 10.1117/1.JEI.25.6.061409

BADRINARAYANAN V, KENDALL A, and CIPOLLA R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481–2495. doi: 10.1109/TPAMI.2016.2644615

王海, 蔡英凤, 贾允毅, 等. 基于深度卷积神经网络的场景自适应道路分割算法[J]. 电子与信息学报, 2017, 39(2): 263–269. doi: 10.11999/JEIT160329

WANG Hai, CAI Yingfeng, JIA Yunyi, et al. Scene adaptive road segmentation algorithm based on deep convolutional neural network[J]. Journal of Electronics &Information Technology, 2017, 39(2): 263–269. doi: 10.11999/JEIT160329

DUAN Jiaxu, LIU Xiaoyan, WU Xin, et al. Detection and segmentation of iron ore green pellets in images using lightweight U-net deep learning network[J]. Neural Computing and Applications, 2020, 32(10): 5775–5790. doi: 10.1007/s00521-019-04045-8

YE Ruifang, PAN C S, CHANG Ming, et al. Intelligent defect classification system based on deep learning[J]. Advances in Mechanical Engineering, 2018, 10(3): 1–7. doi: 10.1177/1687814018766682

ZHANG Shifeng, WEN Longyin, BIAN Xiao, et al. Single-shot refinement neural network for object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 18–23. doi: 10.1109/CVPR.2018.00442.

REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster RCNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031

张烨, 许艇, 冯定忠, 等. 基于难分样本挖掘的快速区域卷积神经网络目标检测研究[J]. 电子与信息学报, 2019, 41(6): 1496–1502. doi: 10.11999/JEIT180702

ZHANG Ye, XU Ting, FENG Dingzhong, et al. Research on faster RCNN object detection based on hard example mining[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1496–1502. doi: 10.11999/JEIT180702

IOFFE S and SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. The 32nd International Conference on Machine Learning, Lille, France, 2015: 448–456.

KINGMA D P and BA J. Adam: A method for stochastic optimization[C]. The 3rd International Conference on Learning Representations (ICLR), San Diego, USA, 2015: 1–13.

邸男, 李桂菊, 陈春宁, 等. 结合归一化差分高斯特征的图像匹配技术研究[J]. 电子测量与仪器学报, 2014, 28(6): 585–590. doi: 10.13382/j.jemi.2014.06.002

DI Nan, LI Guiju, CHEN Chunning, et al. Image matching technology research based on normalized DOG features[J]. Journal of Electronic Measurement and Instrumentation, 2014, 28(6): 585–590. doi: 10.13382/j.jemi.2014.06.002

卢倩雯, 陶青川, 赵娅琳, 等. 基于生成对抗网络的漫画草稿图简化[J]. 自动化学报, 2018, 44(5): 75–89. doi: 10.16383/j.aas.2018.c170486

LU Qianwen, TAO Qingchuan, ZHAO Yalin, et al. Sketch simplification using generative adversarial networks[J]. Acta Automatica Sinica, 2018, 44(5): 75–89. doi: 10.16383/j.aas.2018.c170486

Relative Articles

Supplements(0)

Cited By

Proportional views