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

劉小燕; 李照明; 段嘉旭; 項天遠

doi:10.11999/JEIT190608

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

doi: 10.11999/JEIT190608 cstr: 32379.14.JEIT190608

劉小燕^{1, 2, ,},
李照明¹,
段嘉旭¹,
項天遠³

1.
湖南大學電氣與信息工程學院長沙 410082
2.
電子制造業(yè)智能機器人技術(shù)湖南省重點實驗室長沙 410082
3.
中國科學院空天信息創(chuàng)新研究院北京 100094

基金項目: 國家自然科學基金(61973108, U1913202)，電子制造業(yè)智能機器人技術(shù)湖南省重點實驗室開放基金(IRT2018001)

詳細信息

作者簡介:
劉小燕：女，1973年生，教授，博士生導師，研究方向為圖像處理技術(shù)及其應用、智能建模與控制

李照明：男，1996年生，碩士生，研究方向為圖像處理技術(shù)

段嘉旭：男，1989年生，博士生，研究方向為深度學習與圖像處理技術(shù)

項天遠：男，1985年生，博士生，研究方向為機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: TN911.73; TP391.41
計量
- 文章訪問數(shù): 2671
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- 被引次數(shù): 0
出版歷程
- 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

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

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)

摘要

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行區(qū)分，易發(fā)生裝配錯誤。但是色環(huán)電阻裝配質(zhì)量的人工檢測方法效率低、誤檢率高，而傳統(tǒng)的基于圖像處理技術(shù)的自動檢測方法魯棒性較差，難以解決不同拍攝角度、物距及光照條件下的PCB板色環(huán)電阻檢測問題。針對這一問題，該文提出一種基于卷積神經(jīng)網(wǎng)絡(luò)(CNN)的PCB板色環(huán)電阻自動檢測與定位方法，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)重的交叉熵損失函數(shù)的網(wǎng)絡(luò)訓練方法，較好地解決了復雜光照及場景下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色環(huán)電阻，并通過仿射變換對色環(huán)電阻位置進行垂直校正；最后通過高斯模板匹配方法實現(xiàn)了色環(huán)電阻的色環(huán)定位。采用1270幅PCB圖像對該文方法進行了實驗和驗證，并與傳統(tǒng)的基于形態(tài)學和基于模板匹配的色環(huán)電阻檢測方法進行了對比，結(jié)果表明，該文方法在召回率、準確率及重疊度等性能指標上具有明顯優(yōu)勢，處理速度快，能滿足實際應用要求。
- 圖像分割 /
- 色環(huán)電阻 /
- 卷積神經(jīng)網(wǎng)絡(luò) /
- 印刷電路板
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)

HTML全文

圖 1 PCB圖像數(shù)據(jù)集示例

下載: 全尺寸圖片幻燈片

圖 2 本文算法的總體流程圖

下載: 全尺寸圖片幻燈片

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

下載: 全尺寸圖片幻燈片

圖 4 Max pooling與Upsamping計算過程

下載: 全尺寸圖片幻燈片

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

下載: 全尺寸圖片幻燈片

圖 6 色環(huán)電阻最小外接矩形的確定

下載: 全尺寸圖片幻燈片

圖 7 本文方法與傳統(tǒng)方法的色環(huán)電阻分割與檢測結(jié)果對比

下載: 全尺寸圖片幻燈片

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

下載: 全尺寸圖片幻燈片

圖 9 PCB板上色環(huán)電阻的色環(huán)定位結(jié)果

下載: 全尺寸圖片幻燈片

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

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圖 11 訓練過程中誤差及準確率隨迭代次數(shù)的變化曲線

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表 1 不同檢測方法對圖像1-圖像4中色環(huán)電阻的分割與檢測結(jié)果

方法	圖像分割性能指標					PCB板中色環(huán)電阻實際個數(shù)	檢測出的色環(huán)電阻個數(shù)
方法	Acc	Recall	Precision	IoU	F₁	PCB板中色環(huán)電阻實際個數(shù)	檢測出的色環(huán)電阻個數(shù)
基于形態(tài)學的方法^[5]	0.796	0.575	0.174	0.154	0.260	31	13
基于模板匹配的方法^[8]	–	–	–	–	–	31	4
本文方法	0.966	0.991	0.666	0.660	0.785	31	31

下載: 導出CSV

表 2 不同網(wǎng)絡(luò)層數(shù)時色環(huán)電阻的分割性能指標對比

	平均Acc	平均Recall	平均Precision	平均IoU	平均F₁
W=3	0.985	0.970	0.887	0.870	0.925
W=4	0.991	0.959	0.953	0.924	0.995
W=5	0.985	0.883	0.965	0.865	0.921
W=6	0.979	0.837	0.936	0.805	0.881

下載: 導出CSV

表 3 CNN在測試集與驗證集上的性能指標對比

	平均Acc	平均Recall	平均Precision	平均IoU	平均F₁
驗證集	0.991	0.959	0.953	0.924	0.995
測試集	0.982	0.979	0.851	0.834	0.899

下載: 導出CSV

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