基于深度學習的印刷電路板要素CT圖像檢測技術(shù)研究
發(fā)布時間：2019-07-01 17:06
【摘要】：印刷電路板(Printed Circuit Board,PCB)實現(xiàn)了電子元器件間的電氣連接,是電子產(chǎn)品中不可缺少的重要部件。在生產(chǎn)與使用過程中,PCB經(jīng)常會出現(xiàn)焊盤破損、斷路等問題,造成設備無法正常工作,因此快速檢測與定位故障點對維護電子設備的正常工作具有重要意義。錐束CT(Computed Tomography)成像技術(shù)能夠獲取PCB內(nèi)部結(jié)構(gòu)的高分辨率三維圖像,為非接觸條件下無損檢測PCB缺陷提供了一種新的技術(shù)手段。傳統(tǒng)圖像檢測算法多使用象素級的底層特征,這類特征無語義層次,魯棒性差,受噪聲影響大。深度學習作為近階段出現(xiàn)的一種特征提取技術(shù),具有精簡的模型結(jié)構(gòu)與較強的特征表示能力,能夠提取檢測目標的高層語義信息,為解決基于錐束CT三維圖像的PCB導線、過孔等電路要素檢測問題提供了有力的理論工具。本文以PCB無損檢測的實際應用需求為背景,以實現(xiàn)PCB過孔與導線的自動化檢測為目標,研究基于深度學習的圖像檢測技術(shù)。主要研究內(nèi)容包括深度模型的特征提取技術(shù)、基于深度學習的過孔與導線檢測算法以及算法的軟件實現(xiàn)。論文主要研究成果如下:(1)介紹了深度學習算法的歷史、提出與發(fā)展現(xiàn)狀�；仡櫫藗鹘y(tǒng)淺層網(wǎng)絡模型的發(fā)展過程,總結(jié)了深層網(wǎng)絡與淺層網(wǎng)絡相比所具有的優(yōu)勢。分析了傳統(tǒng)訓練方法在訓練深層網(wǎng)絡時存在的數(shù)據(jù)獲取、局部極值與梯度彌散等問題。重點介紹了深可信網(wǎng)絡的構(gòu)造與對比散度訓練方法,并論述了深可信網(wǎng)絡具有的特點。依據(jù)編碼器與解碼器的有無,將現(xiàn)有的深度模型分為生成型網(wǎng)絡、區(qū)分型網(wǎng)絡與解碼型網(wǎng)絡三類,并對這三類模型的典型結(jié)構(gòu)、改進方法、優(yōu)缺點做了分析與介紹。(2)針對經(jīng)典深可信網(wǎng)絡存在參數(shù)規(guī)模較大的問題,提出了一種基于偽標簽訓練的深度模型。并設計了應用偽標簽訓練降低模型參數(shù)數(shù)量的方法。該方法使用生成型網(wǎng)絡模型提取訓練數(shù)據(jù)的統(tǒng)計特征,使用主成分分析方法降低統(tǒng)計特征的維數(shù),然后將降維后的特征作為對應數(shù)據(jù)的標簽,再使用重新標記的數(shù)據(jù)訓練一個傳統(tǒng)的神經(jīng)網(wǎng)絡,通過調(diào)整隱含層神經(jīng)元節(jié)點數(shù)可以顯著減少神經(jīng)網(wǎng)絡參數(shù)規(guī)模。經(jīng)過在四個機器學習標準測試數(shù)據(jù)集上的測試,本文設計的訓練方法在不損失模型泛化性能的前提下,能夠降低深可信網(wǎng)絡規(guī)模至原始的40%左右。針對無監(jiān)督訓練為什么會有助于有監(jiān)督學習這一問題,通過對偽標簽訓練實驗結(jié)果的分析,提出了一種解釋無監(jiān)督學習原理的觀點。認為無監(jiān)督學習的輸出相比人工數(shù)據(jù)標簽提供了更加豐富的數(shù)據(jù)先驗信息,偽標簽能夠更好地反應數(shù)據(jù)特征,這使模型的代價函數(shù)更加精細,無監(jiān)督訓練在代價函數(shù)的構(gòu)造中起到正則化作用。(3)針對PCB的CT圖像對比度低、噪聲大、存在大量偽影的問題,提出了基于深度學習的PCB過孔與導線檢測方法并進行了軟件實現(xiàn)。該方法采用偽標簽訓練方法構(gòu)造深度模型,通過在樣本圖像上進行訓練,模型可以區(qū)分過孔、背景與12種形狀的導線。所以基于深度學習的方法可以同時檢測過孔與導線要素。對于過孔檢測,可以利用模型輸出結(jié)果直接進行判斷。對于導線檢測,本文根據(jù)導線形狀移動滑動窗口,并以此跟蹤導線軌跡,直至檢測到導線端點為止。實驗結(jié)果表明,基于深度學習的檢測方法能夠有效地克服CT圖像對比度低、噪聲大的問題,并具有一定抗偽影干擾能力。在檢測正確率與效果上都要明顯優(yōu)于Hough變換算法。
[Abstract]:The printed circuit board (PCB) realizes the electrical connection among the electronic components, and is an indispensable part of the electronic product. In the process of production and use, the PCB often has the problems of pad breakage, open circuit and the like, which causes the equipment not to work normally, so the rapid detection and positioning failure point is of great significance to the normal operation of the maintenance electronic equipment. Cone-beam CT (CT) imaging technology can acquire a high-resolution three-dimensional image of the internal structure of a PCB, and provides a new technical means for non-destructive testing of PCB defects under non-contact conditions. The traditional image detection algorithm uses the low-level feature of the pixel level, which has no semantic level, poor robustness and large noise. As a feature extraction technology in the near-phase, the depth study has a thin model structure and a strong feature representation capability, can extract high-level semantic information of the detection target, and aims to solve the PCB lead based on the cone-beam CT three-dimensional image, The detection of the circuit elements such as vias provides a powerful theoretical tool. In this paper, based on the actual application requirement of the non-destructive testing of the PCB, this paper aims to realize the automatic detection of the PCB via and the lead, and studies the image detection technology based on depth learning. The main research contents include feature extraction technology of depth model, through-hole and wire detection algorithm based on depth learning, and software implementation of the algorithm. The main research results are as follows: (1) The history, development and development of the depth learning algorithm are introduced. The development of the traditional shallow network model is reviewed, and the advantages of the deep network and the shallow network are summarized. The data acquisition, local extremum and gradient dispersion of the traditional training method in the training of deep network are analyzed. In this paper, the structure of deep trusted network and the training method of contrast divergence are introduced, and the characteristics of the deep trusted network are also discussed. According to the existence of the encoder and the decoder, the existing depth model is divided into three types: the generation type network, the distinguishing type network and the decoding type network, and the typical structure, the improvement method and the advantages and disadvantages of the three types of models are analyzed and introduced. (2) A deep model based on pseudo-label training is proposed for the problem of large parameter scale in the classical deep trusted network. The method of using pseudo-label training to reduce the number of model parameters is also designed. The method uses the generation type network model to extract the statistical characteristics of the training data, reduces the dimension of the statistical feature by using the principal component analysis method, and then uses the characteristic of the reduced dimension as the label of the corresponding data, and then uses the re-marked data to train a traditional neural network, By adjusting the number of the neuron nodes of the hidden layer, the size of the neural network parameters can be significantly reduced. After the test on four machine learning standard test data sets, the designed training method can reduce the scale of the deep trusted network to the original 40% without losing the generalization performance of the model. Based on the analysis of the experimental results of the pseudo-label training, an idea is put forward to explain the principle of unsupervised learning. It is considered that the output of the non-supervised learning provides more abundant data prior information than the artificial data tag, and the pseudo-label can better reflect the data characteristics, which makes the cost function of the model more precise, and the non-supervised training plays a regularized role in the construction of the cost function. (3) Aiming at the problems of low contrast, large noise and large number of artifacts in the CT image of the PCB, the method for detecting the PCB via hole and the lead wire based on depth learning is put forward and the software implementation is carried out. The method constructs a depth model by using a pseudo-label training method, and the model can distinguish between the via, the background and the 12-shaped wires by training on the sample image. The method of depth learning can simultaneously detect the via and lead elements. For the via detection, it is possible to directly judge the result of the output of the model. For wire detection, this article moves the sliding window based on the wire shape and tracks the wire trace until the wire end point is detected. The experimental results show that the detection method based on depth learning can effectively overcome the problems of low contrast and large noise of the CT image, and has certain anti-artifact interference capability. And the detection accuracy and the effect are obviously better than the Hough transform algorithm.
【學位授予單位】：解放軍信息工程大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41
本文編號：2508653
[Abstract]:The printed circuit board (PCB) realizes the electrical connection among the electronic components, and is an indispensable part of the electronic product. In the process of production and use, the PCB often has the problems of pad breakage, open circuit and the like, which causes the equipment not to work normally, so the rapid detection and positioning failure point is of great significance to the normal operation of the maintenance electronic equipment. Cone-beam CT (CT) imaging technology can acquire a high-resolution three-dimensional image of the internal structure of a PCB, and provides a new technical means for non-destructive testing of PCB defects under non-contact conditions. The traditional image detection algorithm uses the low-level feature of the pixel level, which has no semantic level, poor robustness and large noise. As a feature extraction technology in the near-phase, the depth study has a thin model structure and a strong feature representation capability, can extract high-level semantic information of the detection target, and aims to solve the PCB lead based on the cone-beam CT three-dimensional image, The detection of the circuit elements such as vias provides a powerful theoretical tool. In this paper, based on the actual application requirement of the non-destructive testing of the PCB, this paper aims to realize the automatic detection of the PCB via and the lead, and studies the image detection technology based on depth learning. The main research contents include feature extraction technology of depth model, through-hole and wire detection algorithm based on depth learning, and software implementation of the algorithm. The main research results are as follows: (1) The history, development and development of the depth learning algorithm are introduced. The development of the traditional shallow network model is reviewed, and the advantages of the deep network and the shallow network are summarized. The data acquisition, local extremum and gradient dispersion of the traditional training method in the training of deep network are analyzed. In this paper, the structure of deep trusted network and the training method of contrast divergence are introduced, and the characteristics of the deep trusted network are also discussed. According to the existence of the encoder and the decoder, the existing depth model is divided into three types: the generation type network, the distinguishing type network and the decoding type network, and the typical structure, the improvement method and the advantages and disadvantages of the three types of models are analyzed and introduced. (2) A deep model based on pseudo-label training is proposed for the problem of large parameter scale in the classical deep trusted network. The method of using pseudo-label training to reduce the number of model parameters is also designed. The method uses the generation type network model to extract the statistical characteristics of the training data, reduces the dimension of the statistical feature by using the principal component analysis method, and then uses the characteristic of the reduced dimension as the label of the corresponding data, and then uses the re-marked data to train a traditional neural network, By adjusting the number of the neuron nodes of the hidden layer, the size of the neural network parameters can be significantly reduced. After the test on four machine learning standard test data sets, the designed training method can reduce the scale of the deep trusted network to the original 40% without losing the generalization performance of the model. Based on the analysis of the experimental results of the pseudo-label training, an idea is put forward to explain the principle of unsupervised learning. It is considered that the output of the non-supervised learning provides more abundant data prior information than the artificial data tag, and the pseudo-label can better reflect the data characteristics, which makes the cost function of the model more precise, and the non-supervised training plays a regularized role in the construction of the cost function. (3) Aiming at the problems of low contrast, large noise and large number of artifacts in the CT image of the PCB, the method for detecting the PCB via hole and the lead wire based on depth learning is put forward and the software implementation is carried out. The method constructs a depth model by using a pseudo-label training method, and the model can distinguish between the via, the background and the 12-shaped wires by training on the sample image. The method of depth learning can simultaneously detect the via and lead elements. For the via detection, it is possible to directly judge the result of the output of the model. For wire detection, this article moves the sliding window based on the wire shape and tracks the wire trace until the wire end point is detected. The experimental results show that the detection method based on depth learning can effectively overcome the problems of low contrast and large noise of the CT image, and has certain anti-artifact interference capability. And the detection accuracy and the effect are obviously better than the Hough transform algorithm.
【學位授予單位】：解放軍信息工程大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41
【參考文獻】
相關(guān)期刊論文 前2條
1 彭靜;金亞秋;;Gauss比較函數(shù)與復雜遙感圖像中線狀目標的無偏自動檢測[J];計算機輔助設計與圖形學學報;2007年12期
2 趙波,孫即祥,張學慶,張翠平;使用Radon變換快速檢測直線目標的多應用方法[J];無線電工程;2005年05期
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