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基于卷積神經(jīng)網(wǎng)絡(luò)的印刷電路板色環(huán)電阻檢測(cè)與定位方法

劉小燕 李照明 段嘉旭 項(xiàng)天遠(yuǎn)

劉小燕, 李照明, 段嘉旭, 項(xiàng)天遠(yuǎn). 基于卷積神經(jīng)網(wǎng)絡(luò)的印刷電路板色環(huán)電阻檢測(cè)與定位方法[J]. 電子與信息學(xué)報(bào), 2020, 42(9): 2302-2311. doi: 10.11999/JEIT190608
引用本文: 劉小燕, 李照明, 段嘉旭, 項(xiàng)天遠(yuǎn). 基于卷積神經(jīng)網(wǎng)絡(luò)的印刷電路板色環(huán)電阻檢測(cè)與定位方法[J]. 電子與信息學(xué)報(bào), 2020, 42(9): 2302-2311. doi: 10.11999/JEIT190608
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

基于卷積神經(jīng)網(wǎng)絡(luò)的印刷電路板色環(huán)電阻檢測(cè)與定位方法

doi: 10.11999/JEIT190608
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61973108, U1913202),電子制造業(yè)智能機(jī)器人技術(shù)湖南省重點(diǎn)實(shí)驗(yàn)室開放基金(IRT2018001)
詳細(xì)信息
    作者簡(jiǎn)介:

    劉小燕:女,1973年生,教授,博士生導(dǎo)師,研究方向?yàn)閳D像處理技術(shù)及其應(yīng)用、智能建模與控制

    李照明:男,1996年生,碩士生,研究方向?yàn)閳D像處理技術(shù)

    段嘉旭:男,1989年生,博士生,研究方向?yàn)樯疃葘W(xué)習(xí)與圖像處理技術(shù)

    項(xiàng)天遠(yuǎn):男,1985年生,博士生,研究方向?yàn)闄C(jī)器人控制與信息系統(tǒng)

    通訊作者:

    劉小燕 xiaoyan.liu@hnu.edu.cn

  • 中圖分類號(hào): TN911.73; TP391.41

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

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)
  • 摘要: 色環(huán)電阻是印刷電路板(PCB)中最常用的電子元器件之一,主要依靠色環(huán)的排列順序和顏色等視覺信息進(jìn)行區(qū)分,易發(fā)生裝配錯(cuò)誤。但是色環(huán)電阻裝配質(zhì)量的人工檢測(cè)方法效率低、誤檢率高,而傳統(tǒng)的基于圖像處理技術(shù)的自動(dòng)檢測(cè)方法魯棒性較差,難以解決不同拍攝角度、物距及光照條件下的PCB板色環(huán)電阻檢測(cè)問題。針對(duì)這一問題,該文提出一種基于卷積神經(jīng)網(wǎng)絡(luò)(CNN)的PCB板色環(huán)電阻自動(dòng)檢測(cè)與定位方法,首先采用編碼器-解碼器結(jié)構(gòu)的卷積神經(jīng)網(wǎng)絡(luò)模型及帶有權(quán)重的交叉熵?fù)p失函數(shù)的網(wǎng)絡(luò)訓(xùn)練方法,較好地解決了復(fù)雜光照及場(chǎng)景下PCB板色環(huán)電阻的圖像分割問題;然后采用最小面積外接矩形方法定位單個(gè)色環(huán)電阻,并通過仿射變換對(duì)色環(huán)電阻位置進(jìn)行垂直校正;最后通過高斯模板匹配方法實(shí)現(xiàn)了色環(huán)電阻的色環(huán)定位。采用1270幅PCB圖像對(duì)該文方法進(jìn)行了實(shí)驗(yàn)和驗(yàn)證,并與傳統(tǒng)的基于形態(tài)學(xué)和基于模板匹配的色環(huán)電阻檢測(cè)方法進(jìn)行了對(duì)比,結(jié)果表明,該文方法在召回率、準(zhǔn)確率及重疊度等性能指標(biāo)上具有明顯優(yōu)勢(shì),處理速度快,能滿足實(shí)際應(yīng)用要求。
  • 圖  1  PCB圖像數(shù)據(jù)集示例

    圖  2  本文算法的總體流程圖

    圖  3  編碼器-解碼器結(jié)構(gòu)的卷積神經(jīng)網(wǎng)絡(luò)模型

    圖  4  Max pooling與Upsamping計(jì)算過程

    圖  5  色環(huán)電阻及色環(huán)的定位方法流程圖以及中間過程示意圖

    圖  6  色環(huán)電阻最小外接矩形的確定

    圖  7  本文方法與傳統(tǒng)方法的色環(huán)電阻分割與檢測(cè)結(jié)果對(duì)比

    圖  8  本文方法與Ostu方法[9]的色環(huán)分割結(jié)果對(duì)比

    圖  9  PCB板上色環(huán)電阻的色環(huán)定位結(jié)果

    圖  10  網(wǎng)絡(luò)層數(shù)不同時(shí)的卷積神經(jīng)網(wǎng)絡(luò)模型示意圖(W=3, W=5)

    圖  11  訓(xùn)練過程中誤差及準(zhǔn)確率隨迭代次數(shù)的變化曲線

    表  1  不同檢測(cè)方法對(duì)圖像1-圖像4中色環(huán)電阻的分割與檢測(cè)結(jié)果

    方法圖像分割性能指標(biāo)PCB板中色環(huán)電阻實(shí)際個(gè)數(shù)檢測(cè)出的色環(huán)電阻個(gè)數(shù)
    AccRecallPrecisionIoUF1
    基于形態(tài)學(xué)的方法[5]0.7960.5750.1740.1540.2603113
    基于模板匹配的方法[8]314
    本文方法0.9660.9910.6660.6600.7853131
    下載: 導(dǎo)出CSV

    表  2  不同網(wǎng)絡(luò)層數(shù)時(shí)色環(huán)電阻的分割性能指標(biāo)對(duì)比

    平均Acc平均Recall平均Precision平均IoU平均F1
    W=30.9850.9700.8870.8700.925
    W=40.9910.9590.9530.9240.995
    W=50.9850.8830.9650.8650.921
    W=60.9790.8370.9360.8050.881
    下載: 導(dǎo)出CSV

    表  3  CNN在測(cè)試集與驗(yàn)證集上的性能指標(biāo)對(duì)比

    平均Acc平均Recall平均Precision平均IoU平均F1
    驗(yàn)證集0.9910.9590.9530.9240.995
    測(cè)試集0.9820.9790.8510.8340.899
    下載: 導(dǎo)出CSV
  • 熊光潔, 馬樹元, 聶學(xué)俊, 等. 基于機(jī)器視覺的高密度電路板缺陷檢測(cè)系統(tǒng)[J]. 計(jì)算機(jī)測(cè)量與控制, 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.
    吳福培, 張憲民. 印刷電路板無鉛焊點(diǎn)假焊的檢測(cè)[J]. 光學(xué) 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
    倪堯, 鮑宇. 基于目標(biāo)輪廓幾何特征的電容元件定位方法[J]. 計(jì)算機(jī)工程與科學(xué), 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, 劉良江, 周博文, 等. 一種基于混沌優(yōu)化算法的PCB板元件檢測(cè)方法[J]. 儀器儀表學(xué)報(bào), 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
    姜建國(guó), 王國(guó)林, 孟宏偉, 等. 一種電子元器件組裝結(jié)果檢測(cè)方法[J]. 西安電子科技大學(xué)學(xué)報(bào): 自然科學(xué)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. 軸向色環(huán)電阻質(zhì)量自動(dòng)檢測(cè)系統(tǒng)的設(shè)計(jì)[D]. [碩士論文], 北京理工大學(xué), 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, 蔡英鳳, 賈允毅, 等. 基于深度卷積神經(jīng)網(wǎng)絡(luò)的場(chǎng)景自適應(yīng)道路分割算法[J]. 電子與信息學(xué)報(bào), 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
    張燁, 許艇, 馮定忠, 等. 基于難分樣本挖掘的快速區(qū)域卷積神經(jīng)網(wǎng)絡(luò)目標(biāo)檢測(cè)研究[J]. 電子與信息學(xué)報(bào), 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, 陳春寧, 等. 結(jié)合歸一化差分高斯特征的圖像匹配技術(shù)研究[J]. 電子測(cè)量與儀器學(xué)報(bào), 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
    盧倩雯, 陶青川, 趙婭琳, 等. 基于生成對(duì)抗網(wǎng)絡(luò)的漫畫草稿圖簡(jiǎn)化[J]. 自動(dòng)化學(xué)報(bào), 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
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  • 收稿日期:  2019-08-09
  • 修回日期:  2020-05-26
  • 網(wǎng)絡(luò)出版日期:  2020-06-23
  • 刊出日期:  2020-09-27

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