基於深度學習之工業用智慧型機器視覺系統:以焊點品質檢測為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：139

、訪客IP：3.135.246.193

姓名

佟紹鵬(Shao-Peng, Tung) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於深度學習之工業用智慧型機器視覺系統:以焊點品質檢測為例
(An Industrial AI Vision System based on Deep Learning: an example of solder joint quality inspection)

相關論文

★ 基於虹膜色彩空間的極端學習機的多類型頭痛分類	★ 以多分數加權融合方式進行虹膜影像品質檢定
★ 基於深度學習之工業用智慧型機器視覺系統：以文字定位與辨識為例	★ 基於深度學習的即時血壓估測演算法
★ 基於pix2pix深度學習模型之條件式虹膜影像生成架構	★ 以核方法化的相關濾波器之物件追蹤方法實作眼動儀系統
★ 雷射都普勒血流原型機之驗證與校正	★ 以生成對抗式網路產生特定目的影像—以虹膜影像為例
★ 一種基於Faster R-CNN的快速虹膜切割演算法	★ 運用深度學習、支持向量機及教導學習型最佳化分類糖尿病視網膜病變症狀
★ 應用卷積神經網路的虹膜遮罩預估	★ Collaborative Drama-based EFL Learning with Mobile Technology Support in Familiar Context
★ 可用於自動訓練深度學習網路的網頁服務	★ 基於深度學習方法之高精確度瞳孔放大片偵測演算法
★ 基於CNN方法之真假人臉識別模型	★ 深度學習基礎模型與自監督學習

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

焊點是電子元件和電路板的相會之處，良好的焊接可以讓電路正常運作，然而有瑕疵的焊接會讓整個電路產生不可預期的錯誤，因此焊點的品質對產品的成敗有直接的關係。
過去的自動化視覺檢測系統仰賴人所制定的規則(rule-based)，其修正過程充滿不確定性，本研究通過深度學習以訓練智慧型機器視覺系統，使其能夠辨識焊點的好壞。
Xception是google繼Inception架構而生的神經網路架構，本論文使用Xception對焊點進行訓練。利用合格與品質不良的焊點樣本經過卷積之後所產生的特徵圖之差異訓練智慧型機器視覺系統，使該系統能夠分辨出焊點之良莠。

摘要(英)

Solder joints are the intersection of electronic components and circuit boards. Good soldering allows the circuit to operate normally. However, flawed soldering can cause unpredictable errors in the entire circuit. Therefore, the quality of solder joints has a direct impact on the quality of the electronic product.
In the past, the automated visual inspection system usually functions in rule-based fashion, and the fine-tuning process was full of uncertainty. In this study we apply deep learning paradigm to train the neural network model to identify the quality of the solder joints.
Xception is a neural network architecture which inherits the concept of Inception created by Google. This paper uses solder joints to train Xception. A neural network model trained with the difference between the feature maps produced by the convolution of Pass and Ng solder joint samples can identify the quality of the solder joints.

關鍵字(中)

★ 深度學習
★ 工業檢測
★ 焊點
★ 焊接

關鍵字(英)

★ deep learning
★ industrial inspection
★ solder joint
★ soldering

論文目次

摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章研究背景與動機 1
1.1 研究背景與動機 1
1.2 工業用自動化光學檢察系統(AOI)與影像分類 2
1.3 文獻回顧 3
第二章焊點品質之好壞及瑕疵之分類 5
2.1 焊點分類 5
2.1.1 合格焊點(PASS) 6
2.1.2 包焊(NG) 7
2.1.3 虛焊(NG) 8
2.1.4 空焊(NG) 9
2.1.5 連錫(NG) 10
第三章實驗方法 11
3.1 生成式對抗網路 Generative Adversarial Networks(GAN) 12
3.2 Xception 16
第四章實驗結果及討論 25
4.1 資料收集 25
4.2 資料前處理 26
4.3 資料集 27
4.4 結果 27
4.5 討論 32
4.6 PCB瑕疵監測系統畫面 34
第五章結論 36
REFERENCES 38

參考文獻

REFERENCES

[1] J. Deng, W. Dong, R. Socher. ImageNet: A Large-Scale ierarchical Image Database. In CVPR09, 2009.
[2] MegaFace and MF2: Million-Scale Face Recognition, http://megaface.cs.washington.edu/
[3] ImageNet Large Scale Visual Recognition Challenge (ILSVRC)http://image-net.org/challenges/LSVRC/
[4] A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
[5] K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. In arXiv preprint, arXiv:1409.1556, 2014
[6] Thomas Serre, Lior Wolf, Stanley Bileschi. Robust Object Recognition with Cortex-Like Mechanisms. In IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 3, MARCH 2007.
[7] C. Szegedy, W. Liu, Y. Jia. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1–9, 2015.
[8] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of The 32nd International Conference on Machine Learning, pages 448–456, 2015.
[9] M. Lin, Q. Chen, and S. Yan. Network in network. In arXiv preprint arXiv:1312.4400, 2013.
[10] C. Szegedy, V. Vanhoucke, S. Ioffe. Rethinking the inception architecture for computer vision.In arXiv preprint arXiv:1512.00567, 2015.
[11] K. He, X. Zhang, S. Ren. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.
[12] Francois Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. In arXiv:1610.02357v3 [cs.CV] 4 Apr 2017.
[13] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza. Generative Adversarial Nets. In arXiv:1406.2661v1 [stat.ML] 10 Jun 2014
[14] HFranck Mamalet and Christophe Garcia. Simplifying ConvNets for Fast Learning. In A.E.P. Villa et al. (Eds.): ICANN 2012, Part II, LNCS 7553, pp. 58–65, 2012. Springer-Verlag Berlin Heidelberg 2012..
[15] Andrew G. Howard Menglong Zhu Bo Chen. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. In arXiv:1704.04861v1 [cs.CV] 17 Apr 2017
[16] R. Girshick, J. Donahue, T. Darrell. Rich featurehierarchies for accurate object detection and semantic segmentation. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
[17] Ross Girshick. Fast R-CNN. In arXiv:1504.08083v2 [cs.CV] 27 Sep 2015.
[18] S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In Neural Information Processing Systems (NIPS), 2015.
[19] J. Redmon, S. Divvala, R. Girshick. You Only Look Once: Unified, Real-Time Object Detection. In arXiv:1506.02640v5 [cs.CV] 9 May 2016.
[20] J. Redmon, A. Farhadi. YOLO9000: Better, Faster, Stronger. In arXiv:1612.08242v1 [cs.CV] 25 Dec 2016.
[21] J. Redmon and A. Farhadi. Yolov3: An incremental improvement. In arXiv, 2018. arXiv:1804.02767v1 [cs.CV] 8 Apr 2018.
[22] Navneet Dalal and Bill Triggs. Histograms of Oriented Gradients for Human Detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR′05).
[23] ]David G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. In International Journal of Computer Vision, November 2004, Volume 60, Issue 2, pp 91–110.
[24] E. CANDES, XIAODONG LI, YI MA. Robust Principal Component Analysis. In Journal of the ACM (JACM) Volume 58 Issue 3, May 2011.
[25] CHIH-CHUNG CHANG, CHIH-JEN LIN. LIBSVM: A Library for Support Vector Machines. In ACM Transactions on Intelligent Systems and Technology (TIST) archive Volume 2 Issue 3, April 2011.

指導教授

栗永徽

審核日期

2019-8-6

推文