博碩士論文 105522129 詳細資訊

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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)
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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。
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
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

[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
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